ARL-CR-0775 ● AUG 2015

US Army Research Laboratory

Advanced Composites for Air and Ground 

Vehicles: Final Report

prepared by Nicholas Giannaris

Michigan State University

220 Trowbridge Rd.

East Lansing, MI 48824

under contract W911NF-11-2-0017

Approved for public release; distribution is unlimited.
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Destroy this report when it is no longer needed. Do not return it to the originator.
ARL-CR-0775 ● AUG 2015

US Army Research Laboratory

Advanced Composites for Air and Ground 

Vehicles: Final Report

prepared by Nicholas Giannaris

Michigan State University

220 Trowbridge Rd.

East Lansing, MI 48824

under contract W911NF-11-2-0017

Approved for public release; distribution is unlimited.
ii

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1. REPORT DATE (DD-MM-YYYY)

August 2015

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Final

3. DATES COVERED (From - To)

June 2011–June 2014

4. TITLE AND SUBTITLE

Advanced Composites for Air and Ground Vehicles: Final Report

5a. CONTRACT NUMBER

W911NF-11-2-0017

5b. GRANT NUMBER

5c. PROGRAM ELEMENT NUMBER

6. EDITOR(S)

Nicholas Giannaris

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7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)

Michigan State University

220 Trowbridge Rd.

East Lansing, MI 48824

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US Army Research Laboratory

ATTN: RDRL-WMM-A

Aberdeen Proving Ground, MD 21005-5069

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ARL-CR-0775

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13. SUPPLEMENTARY NOTES

14. ABSTRACT

The following is a summary performed at the Composite Vehicle Research Center at Michigan State University during the 

period of performance ending June 20, 2014, including a one-year no-cost extension. The work described herein is cumulative 

and has been organized mainly into the following research thrust areas: computational modeling; flammability; self-diagnostic 

composite structures; impact resistance; composite joining; multifunctional composite materials; vehicle survivability and 

occupant safety; structural integrity of composites; and biomimetics. The research performed in each of these thrust areas is 

described in this report.

15. SUBJECT TERMS

composites, nanotechnology, material properties, material modeling, nondestructive testing, flammability

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OF ABSTRACT

UU

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OF PAGES

184

19a. NAME OF RESPONSIBLE PERSON

William Spurgeon

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Unclassified

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410-306-1006

Standard Form 298 (Rev. 8/98)

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iii

Contents

List of Figures viii

List of Tables xiv

1. Thrust Area 1A: Computational Modeling 1

Preface 1

1.1 Multiscale Modeling of Polymer Nanocomposites 1

1.1.1 Summary 1

1.1.2 Introduction 2

1.1.3 Task 1: Develop a Microstructure-Inspired Material Model with 

Real and Statistically Equivalent Models for Halloysite Nanotube 

(HNT) Polypropylene Composite 3

1.1.4 Task 2: The 3-D Reconstruction of Exfoliated Graphite 

Nanoplatelets (xGnP) High-Density Polyethylene (HDPE) 7

1.1.5 Task 3: Develop 3-D RVEs with Cohesive-Element-Based Failure 

Models to Predict the Stress-Strain Curve for Nanocomposites 

Under Tensile Loading 11

1.1.6 Summary and Conclusions 23

1.1.7 Outcomes (Journal Papers) 24

1.1.8 References 24

2. Thrust Area 1B: Flammability of Exfoliated Graphite Nanoplatelet 

(xGnP) Composite Materials 30

2.1 Summary 30

2.2 Introduction 30

2.3 Brief Literature Review 31

2.4 Experimental Methods 32

2.5 Test Plan 33

2.6 Materials 34

2.7 Experimental Setup 34

2.8 Testing and Results 35

2.9 Outcomes 37
iv

2.10 Future Objectives 37

2.11 References 38

3. Thrust Area 1C: Structural Joining: Similar and Dissimilar Materials 42

3.1 Summary 42

3.2 Topics Addressed/Successfully Implemented 42

3.3 Deliverables and Outcomes 44

3.4 Acknowledgments 48

4. Thrust Area 2: Self-Diagnostic Composite Structures 49

4.1 Wireless Sensor Networks for Online Monitoring of Heavy-Duty 

Vehicle Systems 49

4.1.1 Summary 49

4.1.2 Introduction 49

4.1.3 Literature Review 51

4.1.4 Experimental Methods 52

4.1.5 Validation 53

4.1.6 Finite Element Modeling 54

4.1.7 Signal Processing Algorithms for Damage Detection 55

4.1.8 Outcomes 56

4.1.9 References 57

5. Thrust Area 3A: Structural Integrity of Composites 58

5.1 A Model for Fatigue Life Prediction of Composite Structures 58

5.1.1 Summary 58

5.1.2 Introduction 58

5.1.3 Fatigue Model Overview 59

5.1.4 PFDM and Its Implementation 60

5.1.5 Experimental 60

5.1.6 Evaluation of PFDM 61

5.1.7 Fatigue Testing with S-2 Glass/SC-15 Epoxy Composite 62

5.1.8 References 65

5.1.9 Publications 66

5.2 Multiscale Damage Modeling 68

5.2.1 Summary 68
v

5.2.2 Introduction 68

5.2.3 Literature Review 68

5.2.4 Approach 69

5.2.5 Results and Discussions 70

5.2.6 Future Work 73

5.2.7 References 74

5.2.8 Publications 76

6. Thrust 3B: Multifunctional Composite Materials 77

6.1 Summary 77

6.2 Functionalization of GnP 78

6.2.1 Elastomeric Functionalization of GnP 78

6.2.2 Edge Modification of GnP 79

6.2.3 Edge Stitching of GnP 80

6.3 Coating of Carbon Fibers with GnP 80

6.4 Fabrication of GnP “Paper” Films 81

6.5 Interlaminar Toughening of Multilayer Vinyl Ester Composites Using 

GnP Paper 82

6.5.1 Moderate-Speed Impact Testing 82

6.5.2 High-Speed Blast Testing 83

6.6 Future Directions 84

6.7 Acknowledgments 84

7. Thrust Area 3C: Vehicle Survivability and Occupant Safety 85

7.1 Scaling Effects in Composites Subjected to Blast Loading 85

7.1.1 Introduction 85

7.1.2 Experimental Methods 85

7.1.3 Materials 86

7.1.4 Experimental Setup 86

7.1.5 Testing and Results 87

7.1.6 Conclusions 87

7.1.7 Outcomes 88

7.2 Developing Triaxial Quasi-3-dimensional (TQ3D) Woven Composites 

with High-Impact Resistance 89

7.2.1 Introduction 89
vi

7.2.2 Development of Q3D Fabrics 89

7.2.3 Manufacture of TQ3D Fabric 90

7.2.4 Testing Results 90

7.2.5 Outcomes 91

7.3 Developing Projection Moiré Assisted Impact Testing 92

7.3.1 Introduction 92

7.3.2 Experimental Setup and Results 92

7.3.3 Validation and Applications 93

7.3.4 Outcomes 94

8. Thrust Area 6: Biomimetics 95

8.1 Enhanced Damage Tolerance in Biomimetic Ceramic Composites 95

8.1.1 Summary 95

8.1.2 Introduction 95

8.1.3 Analytical Model 97

8.1.4 Results and Discussion 99

8.1.5 Inferences 102

8.1.6 Outcomes 102

8.1.7 References 103

9. Thrust Area 7: Design and Manufacturing 105

9.1 Experimental and Numerical Characterization of Flexural Behavior of 

Vacuum-Assisted Resin Transfer Molding (VARTM)-Infused 

Composite Sandwich Structures 105

9.1.1 Summary 105

9.1.2 Introduction 105

9.1.3 Experiments 107

9.1.4 Numerical Modeling 110

9.1.5 Results and Discussion 112

9.1.6 Conclusions 120

9.1.7 References 121

9.2 Mechanical Characterization of Glass and Carbon Fiber Composites 

Fabricated by Resin Infusion 124

9.2.1 Summary 124

9.2.2 Introduction 124

9.2.3 Composite Design and Fabrication 125
vii

9.2.4 Experimental Techniques 127

9.2.5 Results 132

9.2.6 Summary 138

9.2.7 Bibliography 139

9.3 Design and Manufacture of a High-Mobility Multipurpose Wheeled 

Vehicle (HMMWV) Component 140

9.3.1 Summary 140

9.3.2 Introduction 140

9.3.3 Component Identification 140

9.3.4 Materials and Process 142

9.3.5 Trials 145

9.3.6 Results 146

9.4 Interlaminar Reinforcement of Glass Fiber/Epoxy Composites with 

Graphene Nanoplatelets 148

9.4.1 Summary 148

9.4.2 Introduction 148

9.4.3 Methods 150

9.4.4 Testing 151

9.4.5 Results and Discussion 153

9.4.6 Conclusion 163

9.4.7 References 164

Distribution List 167
viii

List of Figures

Fig. 1.1 a) Schematic of serial sectioning, b) slide generated through serial 

sectioning, and c) HNTs imaged with Auriga SEM .................................3

Fig. 1.2 a) 2-D SEM images of HNT polypropylene composite and b) 3-D 

reconstruction of the RVE based on serial sectioning ..............................4

Fig. 1.3 Two-point correlation functions...............................................................5

Fig. 1.4 a) The final mesh for real RVE, b) inclusions’ mesh in real RVE with 

average inclusion volume of 0.025 μm3, and c) inclusions’ mesh in 

statistical RVE ..........................................................................................5

Fig. 1.5 Extracting TPCF of xGnP polymer composite in different directions.....8

Fig. 1.6 RVE with preferred orientation and a known distribution 

of diameters...............................................................................................8

Fig. 1.7 Obtaining material parameters using Mcalibration optimization 

software.....................................................................................................9

Fig. 1.8 a) Stress-strain response (numerical) and b) stress-strain response 

(experimental).........................................................................................10

Fig. 1.9 a) RVE of nanocomposite, b) individual platelet, and c) map of 

stress in x-x direction..............................................................................10

Fig. 1.10 Comparison of stress-strain response of UHMWPE and 

xGnP+UHMWPE.................................................................................11

Fig. 1.11 3-D representation of the spherical coordinates of a randomly 

selected point ........................................................................................12

Fig. 1.12 Examples of 3-D models of nanocomposites with different VFs and 

ARs: a) VF = 1%, AR = 100, b) VF = 1%, AR = 10............................13

Fig. 1.13 Typical traction-separation law for modeling cohesive failure ............13

Fig. 1.14 Comparison of traction-separation response in opening and sliding 

separation [54] ......................................................................................15

Fig. 1.15 Averaged stress-strain curves for RVEs with random distribution and 

orientation of GNP. Particle VFs = 0.5%, 1.5%, 2.5%; AR D/t = 40; 

perfectly bonded GNP. .........................................................................16

Fig. 1.16 Averaged stress–strain curves for RVEs with random distribution and 

orientation of GNP. Particle VFs = 1%; AR D/t = 10, 50, 100; 

perfectly bonded GNP ..........................................................................17

Fig. 1.17 Comparing GNP/HDPE (perfect bonding with different VF and 

constant AR) and GNP/HDPE (cohesive bonding with different VF 

and constant AR) ..................................................................................18

Fig. 1.18 Left: GNP/HDPE (perfectly bonded with different VF and constant 

AR); right: GNP/HDPE (cohesively bonded with different VF and 

constant AR).........................................................................................18
ix

Fig. 1.19 Comparing GNP/HDPE (perfectly bonded with different AR and 

constant VF) and GNP/HDPE (cohesively bonded with different AR 

and constant VF)...................................................................................20

Fig. 1.20 Left: GNP/HDPE (perfectly bonded with different AR and constant 

VF); right: GNP/HDPE (cohesively bonded with different AR and 

constant VF)..........................................................................................20

Fig. 1.21 Study showing the effect of AR (weak bonding) .................................21

Fig. 1.22 Study showing the effect of VF (weak bonding)..................................22

Fig. 1.23 Damage sequence of HDPE/GNP with AR = 100, VF = 1%. Left: 

weak bonding; Right: strong bonding. .................................................23

Fig. 2.1 Cone calorimeter.....................................................................................33

Fig. 2.2 A typical crack pattern that develops after thermal assault....................34

Fig. 2.3 Experimental and modeling plan............................................................35

Fig. 2.4 Stress field as the material degrades from a controlled burn that 

initiates at the top surface .......................................................................36

Fig. 2.5 Modeled crack propagation ....................................................................36

Fig. 2.6 Process involved in burning of composite materials..............................38

Fig. 4.1 The schematic of a centralized wireless sensor network consisting of 2 

entities: the sensor nodes and the base station. The sensor nodes are 

mounted on the structure and connected to embedded or surface-bonded 

transducers. The base station is a remote PC that collects data for 

processing from the sensor nodes. ..........................................................51

Fig. 4.2 a) The extension circuit board connected to the Iris mote using the 

51-pin connector. The sensor node has a total area less than 25 cm2

. 

b) The extension circuit board block diagram showing the sensing and 

actuation modules and the switch that selects which module will be 

connected to the PZT wafer. ...................................................................53

Fig. 4.3 Experimental setup for the validation of the sensing circuit. Two PZT 

wafers with dimensions 8 × 7 mm are bonded to the aluminum plate. 

Sensor node 1 is connected to PZT1 and sensor node 2 is connected to 

PZT2. The outputs of the charge amplifier and the envelope detector 

of sensor node 2 are connected to the oscilloscope. ...............................54

Fig. 4.4 The guided wave signal and its envelope as recorded on the oscilloscope 

are shown in green and blue, respectively. The sampled envelope data 

that is transmitted to the base station and their interpolation are also 

shown. The S0 and A0 modes incident wave packets are indicated. All 

the latter wave packets in the signal are due to reflections from the 

edges of the plate. ...................................................................................54

Fig. 4.5 Lamb wave finite element method model in a single layer carbon 

fiber–reinforced polymer (CFRP) (left) and 8-layer quasi-isotropic 

CFRP laminate (right).............................................................................55
x

Fig. 4.6 Schematic of multilayered composite material and phase velocity 

dispersion curves.....................................................................................55

Fig. 4.7 Imaging of composite plate with impact damage...................................56

Fig. 4.8 Imaging of 450- × 630- × 5-mm composite plate with surface-bonded 

aluminum stiffener to simulate surface damage .....................................56

Fig. 5.1.1 a) A flowchart for the implementation of the PFDM using an 

ABAQUS UMAT. b) The empirical equations used in PFDM in 

determining fatigue life and stiffness/strength degradation. ................60

Fig. 5.1.2 Comparisons of PFDM prediction and experiments. a) PFDM vs. DIC: 

first principal strain (left) and shear strain (right) evolution with 

fatigue cycles on a [02/902]s laminate at Pmax = 25 kN and R = 0. 

b) Failure patterns of [02/902]s (left) and [452/–452]s (right) laminates. 

c) The displacement increases with the number of fatigue cycles, 

[02/902]s at Pmax = 25 kN and R = 0. The prediction-matched 

experiment up to the onset of delamination. The 2 simulations are 

shown in red (with modified values of A and B [19]) and blue 

(with values of A and B [2]). ...............................................................61

Fig. 5.1.3 S-2 Glass/SC-15 epoxy composite fatigue data. a) Master fatigue 

curve for uniaxial loading. b) In-plane shear with R-ratio of 

R = 0, 0.2, and 0.4. ...............................................................................64

Fig. 5.1.4 Gradual degradation of a) the tensile modulus and b) tensile 

strength of S-2 Glass/SC-15 epoxy composite measured at R = 0 ......64

Fig. 5.2.1 Diagram of the various length scales associated with a composite 

structure ...............................................................................................68

Fig. 5.2.2 Proposed multiscale workflow for the free-edge analysis of a 

laminated composite, including the effects of the local 

microstructure......................................................................................70

Fig. 5.2.3 a) The finite element mesh of the microscale RVE and b) the 

applied boundary conditions for the free-edge microscale analysis....70

Fig. 5.2.4 a) Y-stress contour in the [25/–25/90]S laminate under uniaxial 

tension (units are in megapascals). b) The strains extracted from 

the interface and midplane elements...................................................71

Fig. 5.2.5 Contours of the max shear stress and the first principal stress at 

the free edge (X–) generated in the interface and the midplane 

micromodels in a [25/–25/90]s laminate. (Units are in pascals.)........71

Fig. 5.2.6 a) Normal, b) radial shear, and c) axial shear tractions for midplane 

and interface elements along the fiber/matrix interface at the region 

of maximum value. The results are normalized with regard to the 

matrix strength, σt, and the distance to the free edge in the 

X- direction. d) Orientation of the cohesive traction directions. .........72

Fig. 5.2.7 a) Y-stress contours for the [02/90]S laminate and b) the mesoscale 

strains extracted from the interface and midplane elements ...............73
xi

Fig. 6.1 Edge groups that can be attached an xGnP particle................................78

Fig. 6.2 Impact strength of xGnP vinyl ester composites....................................79

Fig. 6.3 Edge modification of an xGn particle.....................................................79

Fig. 6.4 Edge stitching of xGn particles ..............................................................80

Fig. 6.5 Single-fiber interfacial shear strength for different GnP loadings .........81

Fig. 6.6 XGnP papers...........................................................................................82

Fig. 6.7 Three types of laminates studied ............................................................82

Fig. 6.8 Three composite samples (a, b, and c) after blast test ............................84

Fig. 7.1.1 Schematic (top) and photo (bottom) of the Laboratory Blast 

Simulator (LBS)...................................................................................86

Fig. 7.1.2 Comparison of a) strain histories, b) displacement histories, and 

c), a) and b)..........................................................................................87

Fig. 7.2.1 Side view of BQ3D woven composite in a) warp and b) fill directions 

(left), top view of TQ3D woven composite (center), and TQ3D 

braiding machine (right) ......................................................................90

Fig. 7.3.1 Schematic of the overall view (left) and the detailed view (right)......92

Fig. 7.3.2 a) Deformation contours from projection moiré for evenly spaced 

time step of 0.33 mm and b) profile lines for loading .........................93

Fig. 7.3.3 a) Comparison between projection moiré (points) and load cell 

(continuous curve) and b) setup for ballistic impact ...........................93

Fig. 8.1 a) Schematic of staggered architecture of calcium carbonate bricks 

(shown in green) in biopolymer matrix (shown in yellow); b) expanded 

view of the 2-dimensional (2-D) unit cell structure (with plane strain 

infinitesimal deformation) showing the precracked length (L1) and 

instantaneous overlap length (L2) and coordinate systems; c) expanded 

view of the 2-D unit cell structure (with plane strain infinitesimal 

deformation) highlighting the elemental blocks and the original overlap 

length (Lo); d) splitting of parent crack tip for SERR calculations; and 

e) schematic identifying shear and normal force resultants Nc and Qc, 

respectively .............................................................................................98

Fig. 8.2 Variation of interfacial shear stress (at the extremities of the overlap) 

as a function of varying overlap length and precrack. The figure 

in the inset shows the expanded view for lower shear levels in the 

unit cell. ................................................................................................100

Fig. 8.3 SERR ahead of the crack tip with increasing crack in the unit cell......101

Fig. 9.1.1 Layup for the VARTM process.........................................................107

Fig. 9.1.2 a) 3-point loading ASTM C393 standard configuration, S = 150 mm 

and b) 4-point loading ASTM D7249 long beam standard 

configuration, S = 560 mm, L = 100 mm...........................................108

Fig. 9.1.3 Setup of a) 3-point and b) 4-point bending fixture............................109
xii

Fig. 9.1.4 Cylindrical pivot loading and support bar.........................................110

Fig. 9.1.5 a) 3-point bending experiment for H250, 12.7-mm foam core 

sandwich panel, and b) sample under load ........................................113

Fig. 9.1.6 a) 3-point bending experiment for H130, 25.4-mm foam core 

sandwich panel, and b) sample under load ........................................114

Fig. 9.1.7 a) 4-point bending experiment for H250, 12.7-mm foam core 

sandwich panel, and b) sample under load ........................................115

Fig. 9.1.8 a) 4-point bending experiment for H130, 25.4-mm foam core 

sandwich panel, and b) sample under load ........................................116

Fig. 9.1.9 Flexural stiffness for H250, 12.7-mm, and H130, 25.4-mm foam 

sandwich panels .................................................................................117

Fig. 9.1.10 Flexural analysis of sandwich panel subjected to 4-point 

bending: a) longitudinal strains (ε11) and b) equivalent stresses 

(megapascals). (Images obtained as output from 

Digimat/Micross.)............................................................................118

Fig. 9.1.11 Comparison of experimental and simulation-predicted flexural 

response for sandwich panels subjected to 4-point bending with 

12.7- and 25.4-mm core thicknesses................................................119

Fig. 9.1.12 Comparison of flexural failure loads from simulations and 

experiments......................................................................................120

Fig. 9.2.1 (Left) AGY’s S-2 Glass and (right) A&P’s Qiso ..............................125

Fig. 9.2.2 Quasi-isotropic layup for S-glass ......................................................126

Fig. 9.2.3 VARTM setup ...................................................................................126

Fig. 9.2.4 RTM setup .........................................................................................127

Fig. 9.2.5 Tensile test setup ...............................................................................128

Fig. 9.2.6 Compression test setup ......................................................................129

Fig. 9.2.7 Iosipescu test setup ............................................................................130

Fig. 9.2.8 Rail shear test setup ...........................................................................131

Fig. 9.2.9 Flexural test setup..............................................................................132

Fig. 9.2.10 Average ultimate tensile strength comparison.................................133

Fig. 9.2.11 Average elastic modulus comparison..............................................133

Fig. 9.2.12 Average laminate compressive strength comparison ......................134

Fig. 9.2.13 Average laminate compressive modulus comparison .....................135

Fig. 9.2.14 Average failure shear strength from the Iosipescu and V-Notch 

rail shear tests methods....................................................................136

Fig. 9.2.15 Average shear moduli from the Iosipescu and V-Notch rail shear 

test methods.....................................................................................136

Fig. 9.2.16 Average flexural strength comparison.............................................137
xiii

Fig. 9.2.17 Average flexural modulus comparison............................................138

Fig. 9.3.1 Tailgate ..............................................................................................141

Fig. 9.3.2 Rear bumper assembly.......................................................................141

Fig. 9.3.3 Rear bumper strut ..............................................................................142

Fig. 9.3.4 Fabric and cut preform ......................................................................143

Fig. 9.3.5 Preform draping.................................................................................143

Fig. 9.3.6 Final layup stage................................................................................144

Fig. 9.3.7 VARTM setup ...................................................................................144

Fig. 9.3.8 Trial 1 after infusion..........................................................................145

Fig. 9.3.9 Composite bumper strut prototype ....................................................146

Fig. 9.3.10 Highlighted wrinkle in part surface .................................................147

Fig. 9.4.1 Four-point flexural test results for pristine and 0.10, 0.25, and 

0.50 wt% GnP samples......................................................................153

Fig. 9.4.2 Average maximum flexural strength of pristine and 0.10, 0.25, and 

0.50 wt% GnP samples......................................................................154

Fig. 9.4.3 Mode-I fracture toughness (GIc) results of pristine, 0.25 wt%, and 

1.0 wt% GnP samples........................................................................155

Fig. 9.4.4 SEM image of a pristine mode-I fracture surface. Image a) shows a 

macrovoid, and images b) and c) are progressively higher 

magnifications of a void edge, as indicated by the colored boxes.....156

Fig. 9.4.5 Mode-I fracture surface of a 0.25 wt% GnP sample. Image a) shows 

a typical channel region, image b) is a closeup of the fibers on the 

fracture surface, and image c) shows a GnP aggregate lying between 

fibers, as highlighted by a white box.................................................156

Fig. 9.4.6 Intertow region of the 0.25 wt% GnP nanocomposite: a) low and 

b) high magnification.........................................................................157

Fig. 9.4.7 Force-time response of pristine, 0.25 wt%, and 1.0 wt% GnP 

specimens at 20-, 40-, 60-, and 80-J impact energies, respectively...158

Fig. 9.4.8 Energy-time response of pristine, 0.25 wt%, and 1.0 wt% GnP 

specimens at 20-, 40-, 60-, and 80-J impact energies, respectively...159

Fig. 9.4.9 Force-deflection response of pristine, 0.25 wt%, and 1.0 wt% GnP 

specimens at 20, 40, 60, and 80 J impact energies, respectively.......160

Fig. 9.4.10 Dye-penetration inspection (DPI) images of impacted a) 

and back b) surfaces ........................................................................161

Fig. 9.4.11 C-scan images of back surfaces.......................................................162
xiv

List of Tables

Table 1.1 Properties of Halloysite clay nanotube and polypropylene [39] (top) 

and elastic constants measured using real RVE and statistical RVE 

(bottom).....................................................................................................6

Table 1.2 Cohesive zone model parameters for opening and sliding 

modes [54] ..............................................................................................16

Table 1.3 Cohesive parameters for weak bonding...............................................21

Table 3.1 Subproject details.................................................................................42

Table 3.2 Technology transfer student information.............................................47

Table 4.1 Properties of the Iris mote sensing module, processing module, 

and RF module........................................................................................53

Table 5.1.1 Fatigue experiments and PFDM predictions of AS4/3501-6 with 

center hole...............................................................................................62

Table 5.1.2 Mechanical properties of the manufactured S-glass/SC-15 

composite ................................................................................................63

Table 5.1.3 Test matrix for PFDM model parameters for a woven composite ...63

Table 7.1.1 Scaling parameters............................................................................86

Table 7.2.1 Central impact induced bending stiffness.........................................90

Table 9.1.1 Foam core properties [25]...............................................................111

Table 9.1.2 Material properties of plain weave S-2 Glass/SC-15 epoxy 

laminates...............................................................................................111

Table 9.2.1 S-glass and Qiso basic fabric properties.........................................125

Table 9.2.2 Tensile test results...........................................................................134

Table 9.2.3 Compression test results .................................................................135

Table 9.2.4 Shear test results.............................................................................137

Table 9.2.5 Flexural test results.........................................................................138
1

1. Thrust Area 1A: Computational Modeling

Farhang Pourboghrat and Azadeh Sheidaei

Preface

Polymer nanocomposites (PNCs) have demonstrated superior electrical, 

mechanical, physical, and thermal properties and are becoming a major focus for 

both academic and industrial research and development activities. The goal of this 

research project was to develop a multiscale material modeling tool that could 

predict the mechanical properties (stiffness and strength) and failure 

(delamination) of the polymer nanocomposites under different strain rates. To

achieve this goal, the following 3 tasks have been accomplished as milestones for 

this project.

• Task 1: Develop a microstructure-inspired material model with real and 

statistically equivalent models for Halloysite nanotube polypropylene 

composite.

• Task 2: Develop a 3-dimensional (3-D) reconstruction of exfoliated 

graphite nanoplatelets high-density polyethylene.

• Task 3: Develop 3-D representative volume elements with cohesive 

element-based failure models to predict the stress-strain curve for 

nanocomposites under tensile loading.

1.1 Multiscale Modeling of Polymer Nanocomposites

1.1.1 Summary

The focus of this research project was the 3-D reconstruction of Halloysite 

nanotube and nanoplatelet-reinforced polymer composite. The 3-D reconstruction 

was performed using 2 different methods. In the first method, several slices of the 

composite material were obtained using a focused ion beam and scanning electron 

microscopy (SEM). A representative volume element (RVE) of the real material’s 

micro/nanostructures was then constructed by stacking these morphological 

images using VCAT software. In the second method, SEM images of the 

nanocomposite were used to extract statistical 2-point correlation function (TPCF) 

for reconstruction of an RVE of the nanocomposite. The resulting RVEs obtained 

from both methods were meshed for finite element (FE) simulation of 

deformation under tension and shear loadings. The FE results were then used to 

compute information of interest, such as the stiffness tensor of the nanocomposite 

and the stress-strain response of the nanocomposite to external loadings. It was 
2

concluded that the statistical method using TPCF [1] alone can produce an 

approximate microstructure that should be modified using other statistical 

descriptors, such as the 2-point cluster function and lineal path function, to have 

better reconstruction of heterogeneous nanocomposites [2].

1.1.2 Introduction

In recent years, (PNCs) have increasingly gained more attention because of their 

improved mechanical, barrier, thermal, optical, electrical, and biodegradable 

properties [3–5]. With the addition of less than 5 wt% nanoparticles [4], PNCs 

offer a wide range of improvements in moduli [6–8], strength, heat resistance [9], 

and biodegradability [3, 10], as well as a decrease in gas permeability [5,8,11,12]

and flammability [5,8,10,13,14]. 

Although PNCs offer enormous opportunities to design novel material systems, 

development of an effective numerical modeling approach to predict their 

properties based on their complex multiphase and multiscale structure is still at an 

early stage. Molecular dynamics (MD) is becoming a powerful computational tool 

for the simulation of matter at the molecular scale [15–18]. To estimate macrolevel properties of nanocomposites, multiscale homogenization approaches based 

on continuum mechanics are used. The homogenization techniques can be 

categorized into statistical methods, such as weak and strong contrast [19,20];

inclusion-based methods, such as self-consistent or Mori-Tanaka [21]; numerical 

methods, such as finite element analysis (FEA) and asymptotic methods [22];

variational/energy-based methods, such as Hashin-Shtrikman bounds [23]; and 

empirical/semi-empirical methods, such as Halpin-Tsai and classical upper and 

lower bounds (Voigt–Reuss) [24]. Finite element modeling (FEM) has been 

successfully applied to the integrated representative volume elements (RVEs)

with a nanometric secondary phase [25–27]. 

Regardless of the method to reproduce microstructures of the nanocomposite, 

either with well-aligned RVE [26,27] or randomly distributed RVE with a Monte 

Carlo scheme [25], the final RVE cannot entirely represent the actual complex 

and highly heterogeneous nanocomposite structures. Dong et al. [28] developed a 

framework to incorporate the microstructural images, such as SEM micrographs,

into 2-dimensional (2-D) FEM, the so-called object-oriented finite element (OOF) 

technique. The OOF, however, is limited to elasticity and thermal conductivity 

calculations in 2-D microstructures [29–32]. Several experimental and theoretical 

techniques, such as X-ray computed tomography and focused ion beam

(FIB)/scanning electron microscopy (FIB/SEM), are being exploited to obtain 3-D 

microstructure as the input RVE for FEM software [33–35]. Two-point 

correlation function (TPCF) is the simplest statistical correlation functions that 

can convey some information about dispersion and distribution of inclusions in 
3

heterogeneous materials. Recently, Deng et al. [36] presented a statistical work 

based on 2-D realization of the microstructure obtained from SEM images of 

carbon black particle fillers dispersed in synthetic natural rubber. Their statistical 

approach is based on TPCF and 2-point cluster function using an annealing 

technique. 

In this study, unlike previous studies, the 3-D reconstruction of the microstructure 

of polypropylene nanocomposites with 10 wt% (7.2 vf%) HNT fillers was 

achieved using 1) 3-D morphology-based RVE and 2) an RVE of nanocomposite 

constructed using statistical TPCFs. FEA was used to deform the RVEs under 

tension and shear deformations to measure the effective stiffness tensor of the 

Halloysite nanotube (HNT) polymer composite. The numerically predicted results 

obtained from both RVEs were compared against the measured experimental data

to assess the feasibility of the statistical approach in predicting the various 

properties of anisotropic nanocomposites.

1.1.3 Task 1: Develop a Microstructure-Inspired Material Model with 

Real and Statistically Equivalent Models for Halloysite Nanotube 

(HNT) Polypropylene Composite

1.1.3.1 Serial Sectioning of the Nanocomposite Using FIB-SEM

Simultaneous sectioning and imaging of the nanocomposite (10 wt% HNT+PP) 

was performed using a dual-column FIB-SEM (Carl Zeiss Auriga CrossBeam). 

Serial sectioning involved the removal of a known volume of the material by the 

ion beam followed by an incremental analysis with the electron beam. A 

schematic of the serial sectioning and the real images recorded during the FIB 

procedure are presented in Fig. 1.1.

 a) b) c)

Fig. 1.1 a) Schematic of serial sectioning, b) slide generated through serial sectioning, and c) 

HNTs imaged with Auriga SEM
4

The width of each slice was 50 nm; therefore, 50 nm of the nanocomposite was 

milled away with the ion beam followed by an image capture with the electron 

beam. Around 60–100 slices were taken per sample—a process that took 2–3 h. A 

series of 2-D images representing slices or cross sections of the RVE was 

generated through FIB-SEM cutting. The advantage of using serial sectioning is 

to obtain a series of slices with the same reference point allowing an automated 

3-D reconstruction technique to be applied.

1.1.3.2 3-D Reconstruction Using VCAT Software

The 60 serial sectioning bitmap files obtained through serial sectioning was 

imported into VCAT software. The 3-D nanocomposite is represented with gray 

levels between the ranges of colors 0–255 according to the image binarization 

mode 8-bit HSV (hue, saturation, value) color map. By choosing a color threshold 

of (0, 0,100), we find that the image part representation gives the best 

approximation of the dimensions of the cluster of HNTs inside the matrix. A mask 

property is associated with the matrix that will be a color value between 0 and 

255. In this study, each of the 2 phases (matrix and filler) identified in the 

material was given a unique ID to distinguish between the phases inside the 

nanocomposite. The resulting 3-D RVE, shown in Fig. 1.2, possesses the most 

realistic features (size, shape, and distribution) of the actual nanocomposite 

suitable for calculation of its material properties. 

Fig. 1.2 a) 2-D SEM images of HNT polypropylene composite and b) 3-D reconstruction of 

the RVE based on serial sectioning
5

1.1.3.3 Finite Element Mesh of the Reconstructed RVEs

The 3-D reconstructed microstructures were exported to the mesh generation 

software called “vcat2tets” developed by VCAD to create appropriate FE mesh. 

Inclusions have complex shapes, vary in size and orientations, and their surface 

curvature varies from one surface element to another. To take into account the 

influence of these geometric details on the local stress and strain distributions, a 

very fine mesh has been used to define surfaces of the inclusion and in the regions 

between closely spaced inclusions. The mesh was then simplified until a target 

number of tetrahedral elements were achieved. For example, the original FE mesh 

contained 25,000,000 tetrahedral elements; however, by using the “SimpTets”

software developed by VCAD, we reduced the final mesh size to 1,000,000 

tetrahedral elements. The 3-D RVE obtained based on statistical methods using 

the TPCF (Fig. 1.3) was also meshed using vcat2tets software. Figure 1.4 shows 

the FE mesh for both models after the final simplification.

Fig. 1.3 Two-point correlation functions

 a) b) c)

Fig. 1.4 a) The final mesh for real RVE, b) inclusions’ mesh in real RVE with average 

inclusion volume of 0.025 μm3

, and c) inclusions’ mesh in statistical RVE
6

1.1.3.4 Results and Discussions Based on FEA of RVEs

The FE model illustrated in Figure 1.4 is a rectangular cube representing an RVE 

with 5.06 × 5.22 × 4.15 μm3 volume. The created FE mesh was used for 

numerical simulation and calculation of the mechanical response of the material 

under various loading conditions. The tensile modulus of the polymer matrix was 

measured to be 1.3 ± 0.04 GPa. The tensile modulus of HNT was measured 

numerically and experimentally by many researchers [37,38]. HNT, which has 

been used in this study, has the inner and outer diameters and length of 20, 85, 

and 1,000 nm, respectively. Based on the information available in Table 1.1 in Lu 

et al. [38], we have estimated the tensile modulus of HNT to be 140 GPa.

Table 1.1 Properties of Halloysite clay nanotube and polypropylene [39] (top) and elastic 

constants measured using real RVE and statistical RVE (bottom)

Halloysite

Density 2.5 g/cc

Elastic Modulus 140 GPa

Poisson Ratio 0.4

Polypropylene

Density 0.9 g/cc

Elastic Modulus 1.3+ 0.04 GPa

Poisson Ratio 0.3

Real RVE Statistical RVE

Exx 1.76 1.64

Elastic Modulus Eyy 1.54 1.64

(GPa) Ezz 1.93 1.64

νxy 0.33 0.33

Poisson's Ratio νyz 0.34 0.33

νzx 0.33 0.34

In the FEA simulations, it was assumed that both polypropylene matrix and HNT 

would behave as linear elastic materials with perfect interfacial bonding between 

the 2 constituents. This idealization of the material behavior was relaxed in later 

simulations by assuming rate-dependent materials as well as including a third 

interfacial phase between the matrix and the inclusion. The FE simulations were 

carried out using ABAQUS commercial software. A total of 6 simulations of the 

mechanical tests (3 tensile tests and 3 shear tests) were carried out using this 

RVE. The objective was to find the modulus of elasticity and Poisson’s ratios of 

the RVEs in the X, Y, and Z directions. Using the stress and strain distributions 

for each test, we calculated the volumetric average value of stresses and strains, 

and using the generalized Hooke’s law, we constructed the compliance tensor (S), 

as shown in Eq. 1.1. Comparing the constructed compliance tensor for the RVE 

with the compliance tensor for an isotropic material, as given in Eq. 1.2, we 

concluded that this HNT composite was an isotropic material. As a result, the 

elastic modulus in X, Y, and Z directions was found to be Exx = 1.76 GPa, Eyy =

1.53 GPa, and Ezz = 1.93 GPa, as shown in Table 1.1, which summarizes the 

computed properties for real and statistical RVEs. 
7









































− − −

− −

− − −

− − − −

− − −

− − − −

=

0.007 0.002 0.0017 0.005 0.0059 1.7

0.001 0.016 0.024 0.004 1.47 0.006

0.004 0.002 0.014 1.68 0.0033 0.006

0.141 0.15 0.516 0.006 0.0163 0.0007

0.150 0.653 0.136 0.004 0.00757 0.0025

0.568 0.162 0.134 0.0003 0.0025 0.002

S

 GPa-1 (1.1)

















































































+

+

+

− −

− −

− −

=









































12

31

23

33

22

11

12

31

23

33

22

11

0 0 0 0 0 2(1 )

0 0 0 0 2(1 ) 0

0 0 0 2(1 ) 0 0

1 0 0 0

1 0 0 0

1 0 0 0

1

σ

σ

σ

σ

σ

σ

ν

ν

ν

ν ν

ν ν

ν ν

γ

γ

γ

ε

ε

ε

E

(1.2)

The average modulus of elasticity was calculated to be 1.74 and 1.64 GPa for real 

and statistical RVEs, which are within 5% and 8.8% of the experimental value of 

1.8 + 0.03 GPa. The difference between the estimated value using a numerical 

approach and the experimental value is related to the interphase region. Unlike 

micron-sized inclusions, interphase has a large influence on the overall properties 

of the nanocomposite. The perturbation of the polymer chains near the 

nanoparticle creates a constrained region around the nanoparticle [40]. This 

interphase region poses the same length scale as that of the nanoparticle, but its 

properties are different from the host matrix. It has been shown by many 

researchers that taking into account the effect of interphase in evaluating the 

tensile modulus of the nanocomposite will give us higher value for the tensile 

modulus [41].

1.1.4 Task 2: The 3-D Reconstruction of Exfoliated Graphite 

Nanoplatelets (xGnP) High-Density Polyethylene (HDPE)

Following the statistical reconstruction technique described in task 1, a 3-D model 

has been created for the exfoliated graphite nanoplatelet (xGnP)/high-density 

polyethylene (HDPE) nanocomposite. Figure 1.5 shows that this microstructure is 

not isotropic, therefore several TPCF curves in different directions are needed. 

TPCF curves in 0°, 45°, and 90° have been obtained using the SEM image of this 

nanocomposite. 
8

Fig. 1.5 Extracting TPCF of xGnP polymer composite in different directions

Reconstruction has been performed in a couple of steps. First, by controlling the 

Euler angles of platelets during 3-D reconstruction, we matched TPCF curves of 

the reconstructed microstructure with the real microstructure. Next, the size 

distribution of the platelets was incorporated into the simulation using probability 

distribution function of platelets obtained from SEM images; TPCF curves in 

different directions have been matched with the ones from SEM images (Fig. 1.6).

Fig. 1.6 RVE with preferred orientation and a known distribution of diameters

1.1.4.1 xGnP as a Transverse Isotropic Inclusion

Since xGnP is composed of the same material as carbon nanotubes, it shares many 

of their electrochemical characteristics. The platelet shape, however, offers xGnP 

edges that are easier to modify chemically for enhanced dispersion in polymers. 

xGnP is one of the stiffest materials found in nature with the Young’s modulus,

approximately 1,060 GPa in the plane direction. Graphene sheets are interacting 

through van der Waals forces; hence, the xGnP modulus perpendicular to the 

graphene sheets is lower than the other directions. The experimental values for the 

Young’s modulus of xGnP reported in the literature are only for in-plane 

direction, and there is no value for the other direction. Pyrolytic graphite has a 
9

similar molecular structure as xGnP. Blakslee et al. [42] measured the stiffness of 

this substance experimentally and provided the stiffness tensor for it. This 

stiffness tensor has been used as material parameters for xGnP in FE analysis. 

1.1.4.2 A Viscoplastic Material Model for xGnP Polymer Nanocomposite

In this work, we used a viscoplastic model (hybrid model) for the polymer matrix 

[43]. This model has been tested for nanocomposites comprising the ultra-highmolecular-weight polyethylene (UHMWPE) and xGnP. The hybrid model is 

described in detail elsewhere [43]. 

The viscoplastic model has been calibrated using Mcalibration software in order 

to obtain material parameters used in the user-defined material model. Figure 1.7 

shows experimental data compared with the predicted data at different strain rates. 

A uniaxial tensile test has been conducted on a dog-bone specimen made of neat 

polymer at different strain rates. Figure 1.8 shows the results of these simulations 

compared with experimental results. 

Fig. 1.7 Obtaining material parameters using Mcalibration optimization software
10

a) b)

Fig. 1.8 a) Stress-strain response (numerical) and b) stress-strain response (experimental)

The RVE of the nanocomposite comprising xGnP and UHMWPE has been 

reconstructed for 2 different volume fractions (VFs) (2% and 4%). The 

viscoplastic model calibrated above has been used for the polymer, and the 

transverse isotropic elastic model was used for xGnP (Fig. 1.9). Figure 1.10 

shows the stress-strain response of a nanocomposite compared to a neat polymer 

at 1000/s strain rate. The composite becomes stiffer by adding xGnP; however, 

the nanocomposite still exhibits strain rate dependency. 

 a) b) c) 

Fig. 1.9 a) RVE of nanocomposite, b) individual platelet, and c) map of stress in x-x 

direction
11

Fig. 1.10 Comparison of stress-strain response of UHMWPE and xGnP+UHMWPE

1.1.5 Task 3: Develop 3-D RVEs with Cohesive-Element-Based Failure 

Models to Predict the Stress-Strain Curve for Nanocomposites 

Under Tensile Loading

Conducting an experiment at the nanoscale level in order to understand the 

micromechanics of nanocomposites is difficult, if not impossible. Therefore, 

computational and analytical methods must be used to study the mechanics of 

nanocomposites. A deep understanding of the damage and fracture mechanisms of 

nanocomposites is crucial for structural design and practical applications. 

Although damage mechanisms of traditional composites have been widely studied 

in the literature [44–49], there are few studies [49–52] that report on the damage 

and fracture mechanisms of nanocomposites.

In this section, a hierarchical multiscale model to study the damage initiation in 

GNP/HDPE composites will be described. The cohesive zone model (CZM) has 

been adopted to capture the nanofillers debonding. Choosing the appropriate 

cohesive parameters is the most important part in the modeling of debonding in 

nanocomposites. Therefore, the information about interfacial properties of GNP 

and polymer has been obtained from MD simulations. An RVE composed of 

GNPs and polymer matrix was created to study the overall stress-strain response 

of the nanocomposite. The main goal was to perform a systematic computational 

study on the effects of nanofillers/polymer bonding conditions on the macroscopic 

response of GNP/polymer composites for different GNP VFs, aspect ratio (AR), 

and interfacial strength. 
12

1.1.5.1 Representative Volume Element (RVE)

A 3-D RVE consisting of GNP and polymer was created for the nanocomposite. 

The RVE was generated using an in-house-developed C++ algorithm. 

Implementation steps used for developing the RVE with the Monte Carlo 

methodology are defined in the following subsections. Numerical simulations 

were carried out inside a cubic unit cell of constant side length of 1,000 units 

(units may be equally interpreted as nanometers). GNPs were modeled as simple 

discs dispersed inside the RVE. The geometry of each GNP was modeled as 2

parallel circular plates separated by the thickness of the GNP. Each circular plate 

in the volume of the RVE was identified by a normal vector, a center, and a 

radius. To achieve a uniformly random scatter of GNPs using the Monte Carlo 

method, the center of each GNP was selected randomly inside the sample RVE. 

Then, the associated normal vector was specified by means of random 

homogeneous functions to produce uniformly distributed random points on the 

surface of a sphere as following:

2

cos(2 1)

v

Arc u

θ π

φ

 = 

 = − (1.3)

In Eq. 1.3, θ ∈ [0, 2π] and φ ∈ [0, π] are spherical coordinates, as shown in 

Fig. 1.11, and u, v are random variables belonging to [0, 1]. The normal vectors 

thus selected guarantee a uniform random distribution of GNP orientations. For 

generating each GNP, the procedure of random selection of its center and normal 

direction was followed successively, and then the next GNP was identically created.

Fig. 1.11 3-D representation of the spherical coordinates of a randomly selected point

The optimum size of the RVE for each VF and AR was determined by increasing 

the volume of the RVE until the homogenized stress-strain values no longer 

changed significantly. Figure 1.12 shows RVEs of nanocomposites with different 

VF and ARs.
13

Fig. 1.12 Examples of 3-D models of nanocomposites with different VFs and ARs: a) VF =

1%, AR = 100, b) VF = 1%, AR = 10

1.1.5.2 Cohesive Zone Model (CZM)

The behavior of GNPs and the matrix interface is represented by the CZM defined 

in terms of bilinear traction/separation law [53]. However, in the GNP/PNCs, the 

interaction between GNP and polymer is difficult to determine through 

experimental measurements. Thus, we used the results of the MD simulation by 

Awasthi et al. [54] on the interfacial interaction between graphene and 

polyethylene. Figure 1.13 shows a typical traction-separation response with a 

failure mechanism. 

T

Tmax

max δ sep δ

No separation

Complete 

separation

δ

Damage 

starts here.

Fig. 1.13 Typical traction-separation law for modeling cohesive failure

The constitutive relation between these traction stresses and separation is given by

[55]

nn ns nt

ns ss st

nt st tt

n n

s s

t t

t kkk

T t kkk K

t kkk

δ

δ δ

δ

           = =     =           

. (1.4)

a) b)
14

Here, tn is the traction stress in the normal direction, ts, tt are traction stresses in 

the first shear and second shear directions, respectively; K is the nominal stiffness 

matrix, δn is the separation in the normal direction, and δs, δt are separations in 

the first shear and second shear directions, respectively. After completing the 

linear elastic traction-separation, damage will be started. Considering cohesive 

parameters obtained from the MD simulation, the maximum stress criterion was 

used in this study (see Eq. 1.5). Based on this criterion, damage is assumed to 

initiate when the maximum contact stress ratio reaches the value of one. This 

criterion can be represented as

max max max ,, 1 n s t

nst

t t t MAX

ttt

    =

 

. (1.5)

A scalar damage variable, D, represents the overall damage at the contact point. It 

initially has a value of 0 if damage evolution is modeled; D monotonically 

evolves from 0 to 1 upon further loading after the initiation of damage. The 

contact stress components are affected by the damage according to

(1 ) , 0

 

n n

n

n

Dt t

t

t



 − ≥ = 



, (1.6)

otherwise

(1 ) s st Dt = − (1.7)

and

(1 ) t t t Dt = − , (1.8)

where t n , ts , and tt are the contact stress components predicted by the elastic 

traction-separation behavior for the current separations without damage. The 

dependence of the fracture energy on the mixed-mode can be defined based on a 

power law fracture criterion. The power law criterion states that failure under 

mixed-mode conditions is governed by a power law interaction of the energies 

required to cause failure in the individual (normal and two shear) modes. It is 

given by

1 nst

CCC

nst

GGG

GGG

ααα

  ++=



. (1.9)

With the mixed mode, the fracture energy is equal to C G GGG =++ nst when the 

above condition is satisfied. In the above expression, the quantities Gn , Gs ,

and Gt refer to the work done by the traction and its conjugate separation in the 
15

normal, the first, and the second shear directions, respectively. The quantities of 

C Gn , C Gs , and C Gt , which refer to the critical fracture energies required to cause 

failure in the normal, the first, and the second shear directions, should be 

specified. In this work we used α = 1 [56, 57]. For the linear softening (see Fig.

1.14), an evolution of the damage variable [49], D, reduces to

max 0

max 0

( )

( )

f

mm m

f

m mm

D δδ δ

δδδ

− = − , (1.10)

where 2 f C

m G Teff δ = with Teff as the effective traction at damage initiation 

(defined below). max

m δ refers to the maximum value of the effective serration 

attained during the loading history.

222 T TTT eff n s t = ++ . (1.11)

222

m nst δ δδδ = ++ . (1.12)

In the paper by Awasthi et al. [54], separation studies are conducted for both 

normal (traction) and sliding (shear) modes, and the cohesive zone parameters,

such as peak traction and energy of separation, are evaluated for each mode. 

Traction-separation curves for normal debonding and sliding modes are shown in 

Fig. 1.14. This figure shows that the maximum traction for normal mode is higher 

than the one for sliding mode while the separation point is higher in the shear 

mode. Cohesive parameters are listed in Table 1.2.

Fig. 1.14 Comparison of traction-separation response in opening and sliding separation [54]
16

Table 1.2 Cohesive zone model parameters for opening and sliding modes [54]

Fracture Mode Fracture Energy

(MJ/m2

)

Peak Traction

(MPa)

Shear mode 331.650 108.276

Normal mode 246.525 170.616

1.1.5.3 The Effect of the GNP’s Volume Fraction and Aspect Ratio in Perfectly 

Bonded Nanocomposites

RVEs of nanocomposites with 3 different VFs (0.5, 1.5, and 2.5) have been 

created. To study the effect of VF, the AR and the diameter of the inclusions were 

kept constant for all cases as AR = 40 and Dia. = 10 μm. As is shown in Fig. 1.15, 

stiffness increases with an increase in the VF. This is in agreement with the rule

of mixture [53,58]. 

For the analysis of the effect of AR, RVEs of nanocomposites with 3 different 

ARs (10, 50, and 100) have been created. The VF and the diameter of the 

inclusions were kept constant for all cases as VF = 1% and diameter = 4 μm. The 

results show that the nanocomposites’ stiffness increases as the GNP’s AR

increases (Fig. 1.16). This is in agreement with the results obtained by Mortazavi 

et al. [59]. These authors have compared the elastic modulus and thermal 

conductivity of 2-phase random composites with different inclusion types and 

ARs using finite elements and Mori-Tanaka methods. They concluded that the 

elastic modulus and the thermal conductivity of nanocomposites increase by 

increasing the AR.

Fig. 1.15 Averaged stress-strain curves for RVEs with random distribution and orientation 

of GNP. Particle VFs = 0.5%, 1.5%, 2.5%; AR D/t = 40; perfectly bonded GNP.
17

Fig. 1.16 Averaged stress–strain curves for RVEs with random distribution and orientation 

of GNP. Particle VFs = 1%; AR D/t = 10, 50, 100; perfectly bonded GNP

1.1.5.4 Comparing the Effect of the GNP’s Volume Fraction and Aspect Ratio in 

Perfectly Bonded and Cohesively Bonded Nanocomposites

Here, we compare our predicted results for perfectly bonded and cohesively 

bonded composites. The CZM parameters are given in Table 1.2 and are based on 

the results of Awasthi et al. [54] (see Fig. 1.14). Our predicted results for the 

effects of VF, with a constant AR of 40, are reported in Figs. 1.17 and 1.18.
18

Fig. 1.17 Comparing GNP/HDPE (perfect bonding with different VF and constant AR) and 

GNP/HDPE (cohesive bonding with different VF and constant AR)

 Fig. 1.18 Left: GNP/HDPE (perfectly bonded with different VF and constant AR); right: 

GNP/HDPE (cohesively bonded with different VF and constant AR)
19

The 3 graphs comprising Fig. 1.17 show the effect of the VF on the stress-strain 

responses of the perfectly bonded and cohesively bonded GNP/polymer 

composites in comparison to a pure polymer matrix. In all cases, when debonding 

starts to occur, the stress-strain curve for the damaged nanocomposite drops down 

to lower stress levels and deviates from the perfectly bonded nanocomposite. This 

is in agreement with the results of the recent micromechanical model presented by 

Dastgerdi et al. [60]. Figure 1.8 also shows that as VF increases, the difference 

between cohesively bonded and perfectly bonded responses will increase. This 

means that in a nanocomposite with a high VF of GNPs, more inclusions will 

debond compared to those with a low VF. 

For the analysis of the effect of AR, we selected the composite of VF = 1% and 

reported our predicted results in Figs. 1.19 and 1.20 for different ARs (AR = 10, 

50, 100). The graphs depicted in Fig. 19 show the effect of the AR on the stressstrain response of the perfectly bonded and cohesively bonded GNP/HDPE 

nanocomposites as they compare with the response of a pure polymer matrix. In 

all cases, when the debonding starts to occur, the stress-strain curve for the 

damaged nanocomposite drops down to lower stress levels and deviates from the 

perfectly bonded nanocomposites. Figure 1.19 also shows that as AR increases, 

the difference between bonded and nonbonded responses will increase. This is 

due to the high surface area in the higher AR, which leads to the larger fracture 

surface during the debonding. Figure 1.20 shows that by increasing the AR in the 

cohesive model, the stress-strain response does not increase significantly as 

compared to the perfect bonding case.
20

 

Fig. 1.19 Comparing GNP/HDPE (perfectly bonded with different AR and constant VF) 

and GNP/HDPE (cohesively bonded with different AR and constant VF)

Fig. 1.20 Left: GNP/HDPE (perfectly bonded with different AR and constant VF); right: 

GNP/HDPE (cohesively bonded with different AR and constant VF)
21

1.1.5.5 The Effect of the GNP’s Aspect Ratio and Volume Fraction in Weakly 

Bonded Nanocomposites

To analyze the effect of weak bonding, the cohesive zone parameters for weak 

bonding have been selected (Table 1.3). The effect of the AR on the stress-strain 

response of the weakly bonded interface is plotted in Fig. 1.21 for a fixed fillers 

VF of 1% and compared with the response of the pure polymer matrix. This 

figure indicates that 1) in the first stage of deformation, the stiffness of the 

nanocomposite increases as the AR of the nanofillers increases, and 2) in the 

second part of deformation (after yield), the composite will have a lower flow 

stress compared to the host polymer. This indicates that debonding starts in the 

nonlinear region (high strains). Figure 1.21 also shows that the increase in the AR

of the platelets results in a lower flow stress of the composite. In fact, with a fixed 

VF, nanocomposites with higher AR inclusions will have more defects compared 

to those with lower AR inclusions. When nanoplatelets are added to polymers, the 

nanocomposite is expected to have improved stiffness and toughness, the end 

result highly depends on the type of bonding generated between the GNP and the 

host polymer. To improve the interfacial bonding between nanofillers and the host 

polymer, different types of chemical modifiers have been investigated [61,62]. 

Table 1.3 Cohesive parameters for weak bonding

Fracture Mode Fracture Energy

(MJ/m2)

Peak Traction

(MPa)

Shear mode 331.650 30

Normal mode 246.525 40

Fig. 1.21 Study showing the effect of AR (weak bonding)
22

The effect of the VF of the fillers on the overall stress-strain response of the 

nanocomposite with weak interfacial bonding was shown in Fig. 1.22 for a 

constant AR of 40. These results show that by increasing the VF of the nanofiller, 

the nanocomposite will have a lower stress-strain response. This is due to the poor 

bonding between the filler and the matrix that causes more inclusion debonding 

for higher filler VFs. 

Fig. 1.22 Study showing the effect of VF (weak bonding)

To clearly observe the damage sequence of HDPE/GNP nanocomposites from the 

numerical predictions’ viewpoint, the state of damage at the interface during 

loading at strains of 6%, 12%, and 24% is shown in Fig. 1.23. As one can 

observe, the interface damage variable D (called CSDMG) has reached a 

maximum value of 1.0 for many nodes present in the weakly bonded 

nanocomposites compared to the strongly bonded nanocomposites. 
23

Fig. 1.23 Damage sequence of HDPE/GNP with AR = 100, VF = 1%. Left: weak bonding;

Right: strong bonding.

1.1.6 Summary and Conclusions

A hierarchical multiscale model was developed to study the damage initiation in 

polymer/GNP nanocomposites. The CZM was used to model the 

polymer/nanofiller debonding. Results from atomic simulations of GNP pullout and 

debonding tests were used for the CZM. Effects of VF, AR, and fiber-matrix 

interfacial strength on the overall stress-strain response of the nanocomposite were 

investigated. Nanocomposites with perfectly bonded fillers were also modeled for 

comparison with the nanocomposites with cohesive bonding. Results from studying 

the effect of fillers VF and AR in perfectly bonded composites showed that both 

stiffness and toughness will increase with increasing VF and AR.

The effect of the GNPs’ VF on perfectly bonded and cohesively bonded 

composites was compared. As expected, the results showed that when the 

debonding starts to occur, the stress-strain curve for damaged nanocomposites

decreases and deviates from those for the perfectly bonded nanocomposites. 

Results also showed that as VF increases, the difference between cohesively 

bonded and perfectly bonded responses will increase. This implies that in 

nanocomposites with a higher VF, more inclusions will debond compared to 

lower VF. 
24

The effect of AR on the stress-strain response of the perfectly bonded and 

cohesively bonded GNP/HDPE nanocomposites has been studied. It was also 

shown that as AR increases, the difference between bonded and cohesively 

bonded response will increase. The cohesive model has also been tested for 

weakly bonded composites. The results showed that by having weak bonding 

between inclusion and polymer, the resulting composite will have a lower stressstrain response after the yield compared to the host polymer and that the increase 

in the AR of the platelets will lower the flow stress. In fact, with fixed VF, 

nanocomposites with a higher AR will introduce larger defects in the composite 

compared to nanocomposites with a lower AR. By adding nanoplatelets into 

polymers, their stiffness and toughness will be improved provided good bonding 

exists between the nanofillers and the host polymer. Weak bonding between 

nanoplatelets and the polymer matrix may result in a composite with low stiffness 

and toughness. Hence, using different types of chemical modifiers for nanofillers 

to improve the interfacial bonding is necessary. Our simulation results show that 

the stiffness still increases with fillers VF in the case of weak bonding, which 

indicates that debonding becomes noticeably important after the yield point. 

1.1.7 Outcomes (Journal Papers)

1. M. Safaei, A. Sheidaei, M. Baniassadi, S. Ahzi, M. Mosavi Mashhadi, F. 

Pourboghrat, “An interfacial debonding-induced damage model for graphite 

nanoplatelet polymer nanocomposite,” submitted to Computational Materials 

Science (in-review).

2. Tabei, S.A., Sheidaei, A., Baniassadi, M., Pourboghrat, F., Garmestani, H., 

"Microstructure Reconstruction and Homogenization of Porous Ni-YSZ 

Composites," J. of Power Sources, Volume 235, 1 August 2013, Pages 74–

80.

3. Sheidaei, A., Baniasadi, M., Banu, M., Askeland, P., Kuuttila, N, 

Pourboghrat, F., Drzal, L.T., Garmestani, H.,” Mechanical characterization 

of polymer clay halloysite polymer composite using real microstructure and 

statistical models,” Composite Science and Technology, Volume 80, 17 May 

2013, Pages 47–54.

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30

2. Thrust Area 1B: Flammability of Exfoliated Graphite 

Nanoplatelet (xGnP) Composite Materials

Thomas J Pence and Indrek S Wichman

2.1 Summary

This project provided modeling and measurement of the breakdown of composite 

vehicle materials when subjected to flame and general thermal assault. The 

materials considered are load-carrying structural materials. The stress associated 

with load carrying then has an effect on the material degradation process, 

typically causing a certain acceleration in the breakdown. This research is both 

crucial and timely because previous models of material degradation in hostile 

environments do not take into account the interaction between the solid 

mechanics and the thermal/combustion processes. Previous models are one sided,

focusing on one or the other without consideration of the important interaction 

effects. In contrast, our focus on this interaction permits us to simulate the 

material degradation and hence quantify its effect with respect to the service life 

and survivability of a composite vehicle in thermal environments brought on by 

flaming combustion. A particular striking aspect of our study concerns how the 

pattern of material breakdown is highly sensitive to the thermal conduction 

pathways in the material. This has a marked effect on crack formation during 

combustion and degradation. Our experimental work similarly reveals a range of 

crack formation patterns.

2.2 Introduction

This thrust area provides a quantitative ability to assess the flammability of 

composite materials. The key project focuses upon flammability of materials 

incorporating exfoliated graphite nanoplatelets (xGnPs). Currently, the 

flammability of xGnP composites is not well understood in any quantitative sense, 

and the complex microstructural character of composite materials incorporating 

xGnP mitigates against the use of simple combustion theories for describing its 

flammability. The research therefore combines the latest concepts from both 

combustion modeling and solid mechanics to provide a new conceptual 

understanding of how combustion damages the composite material and how this 

breakdown can accelerate the combustion process.

In general, there is a strong need for an accurate description of the process by 

which such materials release combustible volatile gases to the atmosphere in 
31

combustion that is either deliberate (energy conversion) or accidental/hazardous 

(fire). Composite materials are currently being manufactured with a complex 

internal physical structure to offer numerous benefits ranging from structural uses 

in buildings and aircraft or spacecraft to uses in combat vehicles. A requirement 

for these materials is that they also perform well under extreme thermal assault. 

However, assessing composite material performance in this area has received 

little attention compared to performance studies with respect to structural 

integrity, manufacturability, light-weighting, and repairability. This research 

remedies this deficiency by addressing this pressing need. 

The overall research program involves a combination of nonintrusive 

experimental diagnostics, advanced theory, and detailed numerical computations 

to investigate the solid mechanical and thermochemical processes occurring when 

composite materials burn. It provides a means for understanding the key 

mechanisms at the continuum and subcontinuum level that deform the 

combustion-supporting surfaces and rear-surface regions. Some of these key 

mechanisms include crack formation; the development of deformed, peeled layers 

of surface; and the influence of internal thermal stresses on the generation and 

transport of volatile vapors to the flame for combustion. Experimental studies 

employing state-of-the-art facilities so as to permit tightly controlled burning 

processes provide key material parameter measurements. Complementary 

experiments provide a detailed assessment of the accuracy and, hence, predictive 

capability, of the engineering analysis.

2.3 Brief Literature Review

The morphological changes produced when a material degrades in a broad surface 

layer can generate the following: 1) intense, sustained surface combustion that is 

difficult to extinguish [1]; 2) complex, unpredictable surface burn patterns [2]; 3) 

changes in flammability limits, including unquenched burning in low oxidizer 

environments [3]; 4) generation of noxious pollutants [4]; 5) weakened structural 

behavior, i.e., reduced structural support [5]; and 6) diminished resistance to 

impact or blast [6].

Numerous studies have examined the breakdown of heated surfaces. Such

surfaces supply gas-phase fuel for combustion by degrading in a complex manner. 

Only recently have they been examined using nonintrusive diagnostics [7,8]. 

Reviews of propellant modeling are available [9]. Some models have depicted the 

propellant as packed mixed size and type spheres embedded in a thermoplastic 

matrix [10]. In fire safety research, some investigations have emphasized 

models/correlations that characterize material degradation [11–13]. Studies have 
32

been published on modeling solid burning [14]; estimating mass loss rates of 

polymeric materials in fire [15–17]; and modeling specific physical events for 

aircraft fires [18,19]. Aspects of these studies consider 1) evolved combustible 

products [20–23]; 2) the addition of fire retardants to mitigate or suppress 

combustion [22,24]; 3) the incorporation of decomposition models in surface 

burning codes [25,26]; and 4) the rate of ignition of combustible materials [27]. 

Most of the aforementioned studies focus on materials that do not bubble, form 

char, produce microexplosions, or undergo complex time-dependent 

decomposition [25,28]. Many studies use “ideal thermoplastic materials” [28–30],

such as poly(methyl methacrylate), to study thermoplastics or pure research-grade 

cellulose to study charring materials [31,32]. 

For complex materials, the surface is actually a layer of finite thickness. This 

layer often forms cracks and voids that are distributed in a complex. The nature of 

the degraded surface layer structure exhibits enormous variation between 

materials. The surface morphology alters combustion by 1) forming fissures that 

allow exposure of internal material to external heating; 2) allowing volatiles to 

escape from inside the material into the gas to support combustion; and 3) 

forming cracks and voids that weaken the material, making it susceptible to 

physical breakdown into smaller fragments that are more readily consumed by the 

flame. Additional review commentary of surface degradation and combustion can 

be found in refs. 29, 30, and 33–36.

2.4 Experimental Methods

Measurements have been made of the evolving surface morphology in a suite of 

composite samples involving xGnP during pyrolysis and combustion. The MSU 

cone calorimeter (Fig. 2.1) has been employed to evaluate product gas distribution 

and produce a “chemical signature” of the decomposition process by subjecting 

the sample to a specified incoming radiant flux. This facility has been extensively 

used to test commercial materials (ASTM E1354 [37], NFPA 271 [38], ISO 5660

[39]) for evolution of dioxide, carbon dioxide, carbon monoxide, and various 

nitrous oxide (NOx) species. Using radiant energy to heat the samples, we have 

characterized how changes in surface morphology influence volatile release and 

burning rate in a suite of xGnP samples. Because surface morphology is an 

important indicator of the influence of material physical structure (i.e., its 

proclivity for forming voids, cracks, and fissures), we have been able to assess

how in-depth chemicals (volatiles) flow into the gas to enhance combustion as the 

material degrades.
33

Fig. 2.1 Cone calorimeter

2.5 Test Plan

A computational modeling capability has been developed to correlate with the 

experimental characterization work. This model allows continuous refinement by 

making use of existing mathematical techniques and available software. To 

accomplish this, we have replaced certain simplified assumptions that current 

models use in either the gas or solid phase. Gas combustion models that treat the 

flame realistically often make highly simplified assumptions for how reactants are 

supplied by the degrading solid. Conversely, models that describe material 

breakdown make highly simplified assumptions for how heat is supplied to the 

degrading solid interior, how the stresses develop and relieve themselves, and 

how these mechanical factors yield substructural changes that can modify 

pyrolysis. 

Numerical fire models that focus only on the gas and flame are highly developed 

in their ability to simulate fluid mechanical transport, heat transfer, and chemical 

reaction to support combustion. The couplings between these processes are 

understood and permit the application of well-understood analysis techniques at 

length scales ranging from atomistic to continuum. We have used the National 

Institute of Standards and Technology Fire Dynamics Simulator to address the 

phenomenological gas-phase modeling part of this project.

Modeling the solid part has required more attention. Describing material 

degradation and its consequent effect on mechanical performance is not well 

developed for combustion problems involving thermal assault. The standard 

paradigm is instantaneous conversion from solid to gas at a rate determined by 

local temperature. This does not take into account the necessary features of fissure 

formation and crack development, as we have observed in our experiments. The 
34

project therefore has focused on providing such a capability. This has enabled us 

to create new computational procedures that successfully predict how material 

degradation during combustion is dependent upon the structural and thermal 

properties of the composite material. 

2.6 Materials

A wide variety of samples have been analyzed using the cone calorimeter. Figure 

2.2 shows a typical crack pattern that developed after thermal assault. Different 

combinations of xGnP materials have been subjected to controlled combustion in 

the cone calorimeter. In addition, the materials have been simulated for crack 

pattern development during degradation using our developed computational 

routines. Of particular relevance is the effect of thermal conduction upon the 

cracking pattern. Different material designs as obtained by manipulating the 

concentration and placement of xGnP lead to different thermal conduction 

properties of the composite. These, in turn, can give very different patterns of 

crack development for the same far-field combustion profile.

Fig. 2.2 A typical crack pattern that develops after thermal assault

2.7 Experimental Setup

The integration of experiment with theory, computation, and analysis allows 

output from the experiments to provide input to the analysis. It also enables 

rational design of experiments to provide validation for the computational 

methods. Figure 2.3 summarizes how this has been accomplished. In addition to 

making use of commercial combustion simulation software and certain FEA 

procedures for failure analysis, MSU-developed computational mechanics 

methods enable us to incorporate our fundamentally new models for material 

degradation into our engineering simulations. The necessary numerical 
35

procedures have been implemented on a variety of computational platforms. For 

relatively simple simulations, our codes run well on standard laptop machines. 

However, for a full accounting of a material degradation from flame ignition 

through charring combustion, we have found it necessary to use the super 

computer available to us through the MSU Institute for Cyber-Enabled Research 

(http://icer.msu.edu/hpcc). In particular, one of the graduate students involved in 

this project attended a workshop at Iowa State University in the summer of 2013 

to learn the appropriate coding methods to convert our computational software for 

use on the MSU high-performance super computer. At present we are able to 

routinely run on a variety of platforms.

Fig. 2.3 Experimental and modeling plan

2.8 Testing and Results

The experiments indicate that cracks propagate to produce material regions that 

frustrate conduction but enhance convection by providing internal access for the 

surrounding hot gases. Consequently, conduction and convection in the solid are 

modified. As combustion proceeds, material fragments break away, and this 

exposes a fresh (internal) surface for burning. This has been extensively measured 

in our calorimeter experiments.

Figure 2.4 shows the stress field as the material degrades because of a controlled 

burn that initiates at the top surface. This figure can be regarded as a “snapshot” 

of a particular instance in the combustion process. The darkest locations are due 

to the presence of deep penetrating cracks that have formed. The finite element 

method used here tracks crack nucleation and growth in response to stresses that 

develop as the material loses mass during subsurface pyrolysis. The resulting 
36

chemical reactions are heat mediated, and the degree of mass loss is correlated 

with the evolution of the various volatile species in the reaction. In this 

simulation, the cracks advance by a failure criterion based on the maximum 

principal stress, although other failure conditions are also being modeled. The 

computational implementation in this simulation is accomplished by a cell 

removal algorithm.

Fig. 2.4 Stress field as the material degrades from a controlled burn that initiates at the top 

surface

Figure 2.5 depicts the development of a particular crack pattern that evolves with 

time. This is only one of many different crack patterns that can develop for the 

same combustion conditions. Changes in the crack pattern are sensitive to the 

thermal conduction properties, the material stiffness, the pyrolysis conditions, and 

the crack advance criteria as embodied in a failure condition. All of these can be 

altered by the choice of base material in conjunction with the composite 

reinforcing and the use of any particulate additions. The analysis methods that 

have been developed in the course of this project would enable a design engineer 

to evaluate the effect of alternative design choices.

Fig. 2.5 Modeled crack propagation

t t t 

 

 

t t 
37

2.9 Outcomes

The cross-thrust nature of this project makes this research fundamentally new. As 

such, only now are the first papers being readied for publication. However, the 

research results have been communicated directly to Army Research Office 

personnel at the Composite Vehicle Research Center review meetings. In 

addition, Pence and Wichman worked directly with US Army Tank-Automotive 

and Armaments Command personnel on-site in a full-day briefing seminar and 

research workshop that took place September 3, 2013, in Warren, Michigan. Of 

particular interest was the frame-by-frame analysis of material degradation for a 

variety of possible material choices. The implications for the improved design of 

composite systems were then the major topic of the round table–style discussion.

2.10 Future Objectives

This project has demonstrated the feasibility of incorporating flammability 

analysis within the broader structural analysis and standard life-cycle evaluation 

algorithms that provide the usual engineering tools for composite vehicle 

development. The methods that we have developed are fundamentally new. 

Specifically, these methods have required us to consider the combustion-solid 

interaction in a coordinated way that acknowledges how the various physical 

processes interact on a variety of time and length scales, as depicted in Fig. 2.6.

This project has resulted in the development of new capabilities that will enable 

engineers to construct safer, cheaper, and more reliable vehicles. This is 

especially true with respect to military vehicles that operate in harsh environments 

and that, in a combat setting, are routinely subject to combined blast and thermal 

assault. We are eager to move this project from the current successful proof-ofconcept stage (which is inherently research based) to the stage where it combines 

research and development so as to provide improved military vehicle capability. 
38

Momentum 

heat and mass 

transfer in gas

Pyrolysis

Mechanical 

Heat transfer damage

in solid

Reacting gas

Deformation 

and stress 

analysis

Failure 

criteria 

Stiffness 

loss 

(vertical 

only)

Species concentration, 

Temperature distribution

exothermic

Local temperature Mass depletion

Heat 

flux

Boundary condition for 

Evolved 

gas 

species

Thermal property change

Endothermic

Stress effect

Fig. 2.6 Process involved in burning of composite materials

2.11 References

1. Anthony P Hamins, G. Gmurczyk, William L Grosshandler, R. G. Rehwoldt, 

I Vazquez, Thomas George Cleary, Cary Presser, K Seshadri, "Flame 

Suppression Effectiveness, “ NIST SP 861 (1994). See also: A. P. Hamins, 

“Vehicle Fire Research Needs,” NIST Internal Report # NISTIR7406, March 

(2007). 

2. S. L. Olson, F. J. Miller, and I. S. Wichman, "A New Species of Fire: 

Characterizing Fingering Flamelets Using Biological Population Measures," 

Combustion Theory and Modeling 10, 323-347 (2006).

3. S. L. Olson, F. J. Miller, S. Jahangirian, S. and I. S. Wichman, “Flame 

Spread over Thin Fuels in Actual and Simulated Microgravity Conditions,” 

Combustion and Flame, Vol. 156, pp. 1214-1226. (2009).

4. M. Elomaa, L. Sarvaranta, E. Mikkola, R. Kallonen, A. Zitting, C. A. P. 

Zevenhoven and M. Hupa, "Combustion of Polymeric Materials,"Critical 

Reviews in Analytical Chemistry, Vol. 27:3, pp. 137-197. (1997). See also C. 

E. Baukal, Industrial Combustion Pollution and Control, Marcel-Dekker, NY 

(2004).

5. R. Fike, "Strategies for Enhancing the Fire Resistance of Steel Structures 

Through Composite Construction," Ph. D. Thesis, Department of 

CivilEngineering, Michigan State University (2010).
39

6. Li, Q. Li, D. Liu B. Raja and D. Templeton, "Designing Composite Vehicles 

Against Blast Attack". SAE World Congress, Detroit, MI April 16-19 Paper 

2007-01-0137. (2007).

7. T. Parr, and D. Hanson Parr, "Optical Diagnostics of Solid Propellant Flame 

Structures," in Solid Propellant Chemistry, Combustion and Motor Interior 

Ballistics. Progress inAstronautics and Aeronautics, AIAA 185,Eds V. Yang, 

T. B. Brill, W. Z. Ren. (2000).

8. F. Canty, N. Seryn and D. Gramer, "Solid Fuel Pyrolysis Phenomena and 

Regression Rate, Part 2: Measurement Techniques, AIAA 218, Eds. 

Chiaverini and K. K. Kuo, pp. 167-205 (2007).

9. M. Chiaverini, "Review of Solid Fuel Regression Rate Behavior in Classical 

and Nonclassical Hybrid Rocket Motors," Progress inAstronautics and 

Aeronautics, AIAA 218, Eds. Surface Layer Morphology in Flaming 

Combustion 2, M. Chiaverini and K. K. Kuo, pp. 31-125 (2007). See also G. 

Lengelle, "Solid Fuel Pyrolysis Phenomena and Regression Rate, Part 1: 

Mechanisms, ibid, pp. 127-165(2007).

10. Y. Damion, T. L. Jackson, V. Topalain, J. Freund and J. Buskmaster, "Effect 

of Propellant Morphology on Acoustics in a Planes Rocket Motor," 

Theoretical and Computational Fluid Mechanics, Vol. 23, pp. 63-77 

(2009).Buckmaster or other U. Ill. propellant research item.

11. C. Lautenberger, "A Generalized Pyrolysis Model for Combustible Solids". 

Ph. D. Dissertation, Department of Mechanical Engineering, University of 

California at Berkeley, Berkeley, CA (2007).

12. C. Lautenberger and A. C. Fernandez-Pello, "Generalized Pyrolysis Model 

for COmbustible Solids". Fire Safety Journal, Vol. 44, pp. 819-839 (2009).

13. N. Dempsey and M. Janssens, "SFPE Engineering Guide for Estimating 

Material Pyrolysis Properties for Fire Modeling". WPI/SWRI/SFPE Report 

10012011. October (2011).

14. G. T. Linteris, L. Gewuerz, K. McGrattan and G. Forney, "Modeling Solid 

Sample Burning." Manuscript submitted. (2012).

15. F. Kempel, B. Schartel, G. T. Linteris,S. I. Stoliarov, R. E. Lyon, R. N. 

Walters and A. Hofmann-Bollinghaus, "Prediction of the Mass Loss Rate of 

Polymer Materials: Impact of Residue Formation." Combustion and Flame, 

Vol. 159, pp. 2974-2984 (2012).
40

16. G. T. Linteris, M. Zammarano, B. Wilthan, L. Hanssen, "Absorption and 

Reflection of Infrared Radiation by Polymers in Fire-Like Environments," 

Fire and Materials, Vol. 36, pp. 537 -553 (2012). DOI:10.1002/fam1113.

17. G. T. Linteris, "Numerical Simulations of Polymer Pyrolysis Rate: Effect of 

Property Variations." Fire and Materials, DOI: 10.1002/fam.1066 (2010).

18. S. I. Stoliarov and R. E. Lyon, "Thermo-Kinetic Model of Burning," Report 

No. DOT/FAA/AR-TN08/17(2008).

19. S. I. Stoliarov, S. Crowley, R. E. Lyon, G. T. Linteris." "Prediction of the 

Burning Rates of Non-Charring Polymers." Combustion and Flame, Vol. 

156, pp. 1068-1083. (2009).

20. A. A. Stec, P. Fardell, P. Blomqvist, L. Bustamante-Valencia, L. Saragoza, E. 

Guillaume, "Quantification of Fire Gases by FTIR: Experimental 

Characterization of Calibration Systems." Fire Safety Journal, Vol. 46, pp. 

225-233 (2011).

21. J. Luche, T. Rogaume, F. Richard, E. Guillaume, "Characterization of 

Thermal Properties and Analysis of Combustion Behavior of PMMA in a 

Cone Calorimeter." Fire Safety Journal, Vol. 46, pp. 451-461 (2011).

22. L. Bustamente, T. Rogaume, E. Guillaume, G. Rein, J. L. Torero, "Analysis 

of Principal Gas Products of Polyether Polyurethane Foam at Different 

Irradiance Levels." Fire Safety Journal, Vol. 44, pp. 933-940 (2009).

23. R. Ghosh, I. S. Wichman, C. Cramer, C. and R. Loloee, "Time Resolved 

Measurements of Pyrolysis and Combustion Products of PMMA," Fire and 

Materials DOI: 10.1002/fam.129(2012).

24. I. S. Wichman, “Material Flammability, Combustion, Toxicity and Fire 

Hazard in Transportation,” Progress in Energy and Combustion Science,

Vol. 29, pp. 247-299. (2003).

25. K. McGrattan, "Burning Rate." Chapter 14 of updated explanation of NIST 

Fire Dynamics Simulator (FDS), pp. 359-384. (2012).

26. K. B. McGrattan, S. Hostikka and J. E. Floyd, "Fire Dynamics Simulator 

User's Guide." NIST Special Publication 1019. National Institute of 

Standards and Technology (NIST), Gaithersburg, MD, October. (2007). For 

the FDS see http://fire.nist.gov/fds/.

27. V. Babrauskas, Ignition Handbook, Fire Science Publishers, Issaquah, WA. 

Co-published by the Society of Fire Protection Engineers (SFPE) (2011).
41

28. Y. Long, “”A Numerical Study of Flame Spread Over Thin Cellulosic Fuels 

in Microgravity,” Ph.D. thesis, Department of Mechanical Engineering, 

Michigan State University, East Lansing, MI. (2007).

29. I. S. Wichman, “Theory of Opposed-Flow Flame Spread,” Progress in 

Energy and Combustion Surface Layer Morphology in Flaming Combustion 

3 Science, Vol. 18, pp. 553-593. (1992).

30. Y. Long and I. S. Wichman, “Theoretical and Numerical Analysis of a 

Spreading Opposed-Flow Diffusion Flame,” Proceedings of the Royal 

Society A, Vol. 65, pp. 3209–3238 (2009). Also: 

doi:10.1098/rspa.2009.0152.

31. C. Di Blasi, "Modeling and Simulation of Combustion Processes if Charring 

and Non-Charring Solid Fuels,” Progress in Energy and Combustion 

Science, Vol. 19, pp. 71-104. (1993).

32. C. Di Blasi, "The State of the Art of Transport Models for Charring Solids."

Polymer International. Vol. 49, pp. 1133-1146. (2000).

33. B. Moghtaderi, "The State of the Art in Pyrolysis Modeling of 

Lignocellulosic Solid Fuels." Fire and Materials, Vol. 30, pp. 1-30 (2006).

34. G. Zheng, I. S. Wichman, and A. Benard, "Opposed Flow Flame Spread 

Over Polymeric Materials: Influence of Phase Change," Combustion and 

Flame, Vol. 124, pp. 387 -408. (2001).

35. G. Zheng, I. S. Wichman, and A. Benard, "Opposed Flow Ignition and Flame 

Spread for Melting Polymers with Navier-Stokes Gas Flow," Combustion 

Theory and Modeling, Vol. 6, pp. 317 -337. (2002).

36. G. Zheng, I. S. Wichman, and A. Benard, "Energy Balance Analysis of 

Ignition Over a Melting Polymer Subjected to a High Radiation Heat Flux in 

a Channel Cross Flow," Fire Safety Journal, Vol. 38, pp. 229-256. (2003).

37. ASTM E1354-14e1, Standard Test Method for Heat and Visible Smoke 

Release Rates for Materials and Products Using an Oxygen Consumption 

Calorimeter, ASTM International, West Conshohocken, PA, 2014.

38. NFPA 271. Standard Method of Test for Heat and Visible Smoke Release 

Rates for Materials and Products Using an Oxygen Consumption 

Calorimeter. National Fire Protection Association. Quincy, MA; 2009.

39. ISO 5660. Reaction-to-fire tests - Heat release, smoke production and mass 

loss rate - Part 1: Heat release rate (cone calorimeter method) and smoke 

production rate (dynamic measurement). International Organization for 

Standardization. Geneva, Switzerland; 2015.
42

3. Thrust Area 1C: Structural Joining: Similar and Dissimilar 

Materials

Mahmood Haq, Lawrence T Drzal, Alfred Loos, Gary Cloud, Lalita Udpa, 

Eann Patterson, and Nicholas Giannaris

3.1 Summary

The overall work can be classified into 3 fronts: 1) robust multimaterial joining 

(in plane, out of plane, torsional, and large-scale implementations), 2) 

enhancing/tailoring damage resistance in polymer systems and resulting structural 

components (dissimilar materials joints, all-composite battery tray, etc.), and 3) 

development of design tools and database(s)—simulations using integrated 

experimental validation and robust measurement (nondestructive evaluations and 

health monitoring).

Table 3.1 provides a list of ongoing subprojects that are not expanded upon in this 

report.

Table 3.1 Subproject details

Subprojects / Title(s) Time Period

Enhancing Damage Resistance of Structural Composites Using Novel 

Hybrid xGnP/CTBN Platelets: A Computational Materials Approach

(Sponsor: US Army Research Laboratory)

06/2010–05/2011

Damage and Fatigue Enhancements of Novel GnP/CTBN 

Composites: From the Lab to Large-Scale Structural Applications

(Sponsor: US Army Research Laboratory)

06/2011–05/2012

Hybrid Bonding of Dissimilar Materials

(Sponsor: US Army Tank Automotive Research, Development and 

Engineering Center [Fundamental/ Non-Classified Research])

10/1/2012–

Current

Multi-Material Joining: Adhesively Bonded Pi-/T-joints

(Sponsor: US Army Tank Automotive Research, Development and 

Engineering Center [Fundamental/ Non-Classified Research])

10/1/2012–

Current

3.2 Topics Addressed/Successfully Implemented

For brevity, the details of each subproject listed in Table 3.1 are not elaborated in 

this report. Instead, a bulleted list of the topics that were succesfully implemented 

and delivered is provided below. Also, the outcomes of the project in terms of 

publications and training of personnel are briefly provided. Please contact the 

PI/POC for additional details on any of the specific topics discussed herein.

• Determination of effect of surface preparation in bonded multimaterial 

joints.
43

• Out-of-autoclave, cost-effective manufacturing of structural joints and

quality control.

• Damage-induced (manufacturing flaws and impact induced) behavior of 

joints and other structural components.

• Enhancement of fracture toughness and impact resistance of joints using 

novel hybrid graphene/CTBN (rubber) particles. Other micro-/nanomodifications include nanoclay and glass micro-spheres.

• Fatigue behavior of multimaterial joints and novel tailorable materials: 

experimental evaluation of the effect of nano- and micro-modifications on 

resulting structural components.

• Notched behavior of structural composites and resulting joints: 

experimental and numerical evaluation.

• Tailored fiber placement and its efficiency in rapid manufacturing, lightweighting, and development of robust joints and other structural 

composites.

• Environmental testing: 1) structural behavior at extreme temperatures (32 °F

and 120 °F) and 2) moisture absorption (soak test + periodic testing).

• Novel hybrid fastening system (patented technology developed by Dr 

Gary Cloud) that produces dissimilar joints with numerous possibilities 

including “zero-slip” behavior, reduced/delayed delaminations under load,

and enhanced load-carrying capacities relative to conventional mechanical 

fastening techniques.

• Effect of curing-induced shrinkage of resins and its effect on strengths of 

adhesive joints and other structural components.

• Nondestructive evaluation and health monitoring of multimaterial joints 

and structures.

• Large-scale light-weighting structural applications: 1) composite I-beams 

for bridging applications and their flexural behavior and 2) prototypes of 

bumper attachments using Pi/T-joints and simulations regarding the 

feasibility of this work.
44

3.3 Deliverables and Outcomes

Provided below is a list of outcomes in the form of book chapters, publications, 

conference presentations, and proposals. This list is preliminary and may be 

missing some publications.

Book Chapters:

[1] Chapter 22: Haq M., Khomenko A., Udpa L., Udpa S. (2014). “Fiber BraggGrating Sensor Array for Health Monitoring of Bonded Composite LapJoints.” Experimental Mechanics of Composite, Hybrid, and Multifunctional 

Materials, Volume 6, pp. 189-195. Springer, New York, USA.

[2] Chapter (TBA): Khomenko A, Koricho EG, Haq M (2014). “Monitoring the 

effect of micro-/ nano – fillers on curing-induced shrinkage in epoxy resins” 

Fillers and Reinforcements for Advanced Nanocomposites, Wood Head 

Publisher, the part of Elsevier. Under Final Review.

[3] Chapter (TBA): Koricho EG, Khomenko A, Haq M (2014). “Influence of 

Micro-fillers on Impact Response of Glass Fiber Reinforced Plastic (GFRP) 

Composite” Fillers and Reinforcements for Advanced Nanocomposites, 

Wood Head Publisher, the part of Elsevier. Under Final Review

Journals (Published + Submitted):

[4] Karpenko O., Haq M., Khomenko A., Udpa L., Udpa S. (2014). “Lamb 

wave based monitoring of delamination growth in mode I and mode II 

fracture tests.” Fracture and Fatigue, 7:33-43.

[5] Umer R, Waggy EM, Haq M, Loos AC. (2012) “Experimental and 

Numerical Characterization of Flexural Behavior of VARTM-Infused 

Composite Sandwich Structures,” Journal of Reinforced Plastics and 

Composites, 31: 67-76.

[6] Koricho EG, Khomenko A, Fristedt T, Haq M (2014), “Innovative Tailored 

Fiber-Placement Technique for Enhanced Damage Resistance in Notched 

Composite Laminate,” Manuscript submitted to Journal of Composite 

Structures, Under Review.

[7] Koricho EG., Khomenko A, Haq M., “Impact Behavior of Glass Microbubbles Reinforced Thermoplastic Adhesive Joints,” Manuscript submitted 

to Journal of Composite Structures, Under Review.

Journals (Manuscripts under Internal Review, to be submitted):

[8] Haq M., Koricho E.G. and Khomenko A. “Experimental, FEA and 

Analytical Studies of Multi-Materials Joining for Automotive Applications”. 

To be submitted (December 2014) to Journal of Adhesion Science and 

Technology.

[9] Haq M., Khomenko A., Udpa L., Udpa S., “Fiber Bragg-Grating Array for 

Health Monitoring of Bonded Composite Lap-joints”. To be submitted

(December 2014) to Composite Structures.
45

[10] Haq M., Cloud G. “Novel Hybrid Fastening System” To be submitted

(December, 2014) to Composite Structures.

[11] Haq M, Patterson EA, Drzal LT. “Pull-out Behavior of Adhesively Bonded 

Composite Pi-joints: Numerical Modeling of Damage and Validation with 

Experiments,” Journal of Reinforced Plastics and Composites. 

Communicated Aug. 2014.

[12] Haq M., Patterson E.A., Drzal L.T. “Impact Behavior of Adhesively Bonded 

Structural Composite T-joints,” To be submitted (December, 2014) to 

Composite Structures.

[13] Haq M., Drzal L.T., Patterson E.A. “Health Monitoring of Composite Pi/Tjoints using FBG Sensors,” To be submitted (December, 2014) to Journal of 

Reinforced Plastics and Composites

Conference Publications:

[14] Dib G, Karpenko O, Koricho E, Khomenko A, Haq M, Udpa L, Udpa SS, 

“Feasibility of PZT Ceramics for Impact Damage Detection in Composite 

Structures.” 41st Annual Review of Progress in Quantitative Nondestructive 

Evaluation (QNDE 2014), Boise, ID, USA. July 20-25, 2014.

[15] Karpenko O, Dib G, Koricho E, Khomenko A, Haq M, Udpa L, Udpa SS. 

“Structural Health Monitoring of Adhesively Bonded Lap-Joints During 

Fatigue Testing” 41st Annual Review of Progress in Quantitative 

Nondestructive Evaluation (QNDE 2014), Boise, ID, USA. July 20-25, 2014.

[16] Haq M., Khomenko A., Cloud G. “Novel Hybrid Fastening System with 

Nano-additive Reinforced Adhesive Inserts,” SEM 2014 Annual Conference 

& Exposition on Experimental and Applied Mechanics, Greenville, SC USA 

June 2-5, 2014. 

[17] Koricho E.G., Khomenko A., Haq M, “Fatigue Behavior of Glass-Bubbles 

Modified Adhesively Bonded Composite Joints For Automotive 

Applications,” SEM 2014 Annual Conference & Exposition on Experimental 

and Applied Mechanics, Greenville, SC USA June 2-5, 2014.

[18] Khomenko A., Koricho E.G., Haq M., “Measurement of Curing-induced 

Shrinkage in Nano-modified Resins and its Effect on Tensile strengths,” 

SEM 2014 Annual Conference & Exposition on Experimental and Applied 

Mechanics, Greenville, SC USA June 2-5, 2014..

[19] Koricho E.G., Khomenko A., Haq M., “Innovative Tailored FiberPlacement Technique for Enhanced Damage Resistance in Notched 

Composite Laminate,” 1st Int. Conference on Mechanics of Composites 

(MECHCOMP2014), Long Island, NY State, USA, 8-12 June 2014. 

[20] Koricho E.G., Khomenko A., Haq M., “Impact Behavior of Glass Microbubbles Reinforced Thermoplastic Adhesive Joints,” 1st Int. Conference on 

Mechanics of Composites (MECHCOMP2014), Long Island, NY State, USA 

8-12 June 2014.
46

[21] Ripberger J., Khomenko A., Haq M., Gianaris N., Cloud G., “A Tailorable 

Fastening System for Dissimilar Material Joining.” 13th Annual Society of 

Plastic Engineers (SPE) and Automotive Composites Conference & 

Exhibition (ACCE), Novi, MI, USA, Sept. 2013.

[22] Haq M., Drzal L.T., Cloud G., “Metal-Composite Bonded Pi- joints.” SEM 

2013 Annual Conference & Exposition on Experimental and Applied 

Mechanics, Chicago, IL, USA, June 3-5, 2013.

[23] Haq M., Cloud, G., “Flexible Hybrid Fastening System.” SEM 2013 Annual 

Conference & Exposition on Experimental and Applied Mechanics, 

Chicago, IL, USA, June 3-5, 2013.

[24] Haq M., Khomenko A., Udpa L., Udpa S., “Fiber Bragg-Grating Array for 

Health Monitoring of Bonded Composite Lap-joints.” SEM 2013 Annual 

Conference & Exposition on Experimental and Applied Mechanics, 

Chicago, IL, USA, June 3-5, 2013.

[25] Karpenko O., Haq M., Udpa L., Udpa S., “Lamb Wave Based Monitoring of 

Delamination Growth in Mode I and Mode II Fracture Tests.” SEM 2013 

Annual Conference & Exposition on Experimental and Applied Mechanics, 

Chicago, IL, USA, June 3-5, 2013.

[26] Haq M., Umer R., Loos A.C., Drzal L.T., “Manufacturing and Impact 

Behavior of Sandwich Composites with embedded Graphene Platelets.” The 

19th International Conference on Composite Materials, Montreal, Canada. 

July 28-August 2013.

[27] Haq M., Karpenko O., Khomenko A., Udpa L., Udpa S., Gianaris N., 

“Guided Wave Inspection of Bonded Dissimilar Material Joints.” 

ISEM2013-International Symposium on Applied Electromagnetics and 

Mechanics, Quebec, Canada, July 31-August 2, 2013.

Conference Publications (Accepted for Presentation in 2014):

[28] Koricho E.G., Khomenko A., Haq M., “Fatigue Behavior of Bonded Pi/Tjoints,” 2014 ASC 29 / US-Japan 16 / ASTM D30 Conference, San Diego, 

CA, USA, 8-10 Sept. 2014. (Accepted)

[29] Khomenko A., Koricho E.G., Haq M., “Fiber Bragg-Grating Sensors for 

SHM and FEM of In-Service Bonded Multi material Pi-joints” 2014 ASC 29 

/ US-Japan 16 / ASTM D30 Conference, San Diego, CA, USA, 8-10 Sept. 

2014. (Accepted).

[30] Khomenko A, Koricho EG, Cloud G, Haq M. "Torque-Load Monitoring in 

Bolted Joints using Innovative ‘MoniTorque’ Bolts," The Composites and 

Advanced Materials Expo (CAMX), Orlando, FL, October 13-16, 2014.

[31] Koricho EG, Khomenko A, Haq M, Fristedt T, Belingardi G, Martoran B., 

"Development of Composite Control Arm for Automotive Applications 

using Tailored Fiber Placement (TFP),"The Composites and Advanced 

Materials Expo (CAMX), Orlando, FL, October 13-16, 2014.
47

[32] Koricho EG, Khomenko A, Belingardi G, Martoran B,Drzal LT, Haq M. 

"Influence of Hybrid (Micro- and Nano-) Fillers on Impact Response of 

GFRP composite," The Composites and Advanced Materials Expo 

(CAMX), Orlando, FL, October 13-16, 2014.

Proposals and White Papers:

[P1] Haq M., Cloud G., “Flexible Hybrid Fastening System for Multi-Material 

Joining,” Submitted to TARDEC on Jan. 14, 2013. Result- Not funded, but few 

elements of the proposal were implemented in Multi-Material Pi-joint work.

[P2] Haq M., Cloud G., “Flexible Hybrid Fastening System for Dissimilar 

Material Joining,” Submitted to DoE on April, 2013. Result- Concept Paper 

was accepted, final proposal was not funded, but a few elements of the 

proposal were implemented in Multi-Material Pi-joint work.

[P3] Haq M., Drzal L.T., Udpa L., Loos A.C., Gianaris N., “Active, Tailorable 

Adhesives for Dissimilar Material Bonding, Repair and Reassembly,” 

Submitted to U.S. Department of Energy (DoE), Awarded $600k, October 1, 

2013 to December 2016. NOTE: This proposal is intellectually entirely 

different than the multi-material joining projects funded by TARDEC/ARL. 

Nevertheless, the findings/ limitations from the TARDEC funded MMJ 

projects were used to develop innovative solutions as proposed to DoE.

Technology Transfer (Trained Workforce): Undergraduate students mentored and 

trained in manufacturing and experimental testing of composites and 

multimaterial joining. All undergraduates trained thus far are US citizens or 

permanent residents. The students’ status and current employment information is 

found in Table 3.2.

Table 3.2 Technology transfer student information

Student Status Employment/Internship

Alexander Bonnen

Sophomore, honor student, 

currently working on MMJ 

projects

Williams International and 

Stryker Inc.,

Benjamin Bosworth Graduated Dec. 2013 Pratt & Miller (full-time)

Christopher Churay Sophomore, left group in May, 

2013 MSU Formula 1 SAE team

Stephanie Fierens

Sophomore, honor student, 

currently working on MMJ 

projects

Not Available

Morgan Hoxsie Junior, worked in summer 2013 Not Available

Caroline Williams Senior, worked in summer 2013 Not Available

Jack Potterack Senior, worked in summer 2013 Not Available

Jacob Ripberger Sophomore, worked in summer 

2013 Not Available

Mark Dawson Graduated May 2013 ALCOA (full-time)

Saurabh Sinha Junior, left group in Aug. 2013 Toyota

Erik Stitt Graduated Mar. 2014. Schlumberger (full-time)

Gerges Dib PhD, Graduated July 2014 Pacific Northwest National 

Laboratory (full-time)
48

3.4 Acknowledgments

The PI (Dr Mahmood Haq) would like to acknowledge the sponsors for their 

support. The PI would like to thank the co-PIs, postdocs, students, and other 

collaborators for their tremendous support and teamwork to achieve the desired 

deliverables. The experimental facilities of the Composite Vehicle Research 

Center (CVRC) and Michigan State University are duly acknowledged. The 

support of CVRC staff in preparation of text fixtures is acknowledged. Most 

importantly, the feedback from the program managers from all sponsors, 

including but not limited to Dr Doug Templeton, Dr Bill Spurgeon, Dr Roger 

Crane, Dr Maureen Foley, Dr Richard Gerth, Mr Vlad Gendlin, Mr Dani 

Shaffren, Mr Demetrios Tzelepis, and Ms Lynne Krogsrud are highly appreciated. 

Their continuous feedback and interest has provided direction and increased the 

productivity of the funded research.
49

4. Thrust Area 2: Self-Diagnostic Composite Structures

Lalita Udpa, Mahmood Haq, Gerges Dib, and Oleksii Karpenko

4.1 Wireless Sensor Networks for Online Monitoring of HeavyDuty Vehicle Systems

4.1.1 Summary

The use of wireless sensor networks for structural health monitoring has the 

potential to significantly increase safety and reduce manufacturing and 

maintenance costs. Wireless sensor networks use low-footprint “smart” active 

sensor nodes that are permanently mounted on the structure. The sensor nodes 

should be self-powered and have the means to detect changes in the structure 

indicating impending hazards and to communicate wirelessly with other sensor 

nodes or base stations when anomalies are detected. They should also have 

processing power to be able to decide if a detected event indicates a hazard before 

transmission. This report presents the sensor node hardware and software 

implementation for passive and active sensing of elastic guided waves. A 

distributed control algorithm is presented for controlling a wireless sensor 

network from a base station. A proof-of-concept implementation of the sensor 

nodes for damage detection in a plate is demonstrated.

4.1.2 Introduction

Structural health monitoring (SHM) serves to improve safety and reduce 

maintenance costs by providing real-time information about the structure’s 

integrity and warning about impending hazards. The integration of SHM systems 

within industrial structures can change current safety and inspection practices, 

eliminating the need for regularly scheduled inspections and migrating toward 

condition-based inspections. An SHM system uses a network of sensors that are 

permanently surface mounted or embedded in the structure. 

The ultrasonic guided wave method using built-in thin piezoelectric films has 

emerged as a promising option for locating and characterizing damage in SHM. 

Guided waves are elastic stress waves that are guided within the confines of a 

structure’s surface when one dimension of the structure is smaller than the 

propagating wavelength. A guided wave travels along the surface of the structure 

with nonpropagating perturbations (standing wave) along the thickness. The 

propagating wave energy diffuses only in 2 dimensions, reducing attenuation and 
50

allowing the wave to propagate for a longer distance. This allows the monitoring 

of a large structural area using a sparse network of fixed sensors, which makes it 

attractive in SHM applications.

A main challenge in implementing an SHM system using guided waves is to 

transfer data from the lead-zirconate-titanate (PZT) sensor network to a base 

station. Connecting the sensors directly through cables and wires has a high 

installation and material cost. Wireless sensor networks (WSNs) offer a promising 

solution for continuous SHM. WSNs are inherently highly scalable and 

configurable and do not require high installation and maintenance cost. A sensing 

device is connected to the PZT sensor to acquire and digitize the measured 

waveforms. Because of the lack of physical wiring, those devices need to be selfpowered. This limits the amount of power available to them, requiring the use of 

low-power electronics, which limits the computational performance. This poses a 

challenge for actuating high-power guided wave signals and also data acquisition 

of high-frequency signals, which requires sampling rates higher than those 

supported by current low-power wireless sensing devices.

The simplest architecture of a WSN for SHM is the centralized architecture, as 

shown in Fig. 4.1. There are 2 different entities in this architecture: the sensor 

nodes and the base station. The sensor nodes are smart low-power devices 

equipped with signal conditioning and data acquisition devices, microcontroller, 

digital memory, power supply, and a radio. Multiple sensor nodes are deployed on 

the structure and connected to the PZTs, forming a wireless sensor network. 

Because of power scarcity, the computational speed, wireless transmission power, 

and data acquisition speed are limited. The presence of a microcontroller enables 

us to control the sensor node, thus making WSNs highly configurable and 

automated. This is achieved by programming the microcontroller using software. 

One base station communicates and controls the network of sensor nodes 

mounted on the structure. The base station has a gateway that is responsible for 

transferring data from the WSN to a PC and vice versa. The PC has a user 

interface where an administrator could control and configure the network or 

individual sensor nodes. It also has visualization and prognostic tools, indicating 

the presence of potential hazards in the structure.
51

Fig. 4.1 The schematic of a centralized wireless sensor network consisting of 2 entities: the 

sensor nodes and the base station. The sensor nodes are mounted on the structure and 

connected to embedded or surface-bonded transducers. The base station is a remote PC that 

collects data for processing from the sensor nodes.

4.1.3 Literature Review

The use of guided waves as the inspection method is attractive for SHM 

applications since such waves can be excited at one point and they can propagate 

long distances with little attenuation [1]. This allows the inspection of large areas 

in a structure using minimal sensing points. PZT piezoelectric wafers are used as 

transducers for the excitation and sensing of guided waves in the structure. Those 

PZT wafers are most commonly used in SHM because of their low cost, low 

weight, small size, and the practicality of surface bonding or embedding them in a 

structure [2].

There is not much research conducted in the integration of in-situ wireless 

technology for continuous monitoring using guided waves. Many sensor nodes for 

wireless SHM systems have been developed since the late 1990s. A summary 

review of sensor nodes developed for SHM until the year 2005 can be found in 

refs. 3 and 4. Most of the described sensor nodes do not include an actuation 

interface and are used for measuring the frequency response of structures under 

vibration using accelerometers, requiring the acquisition of signals with 

bandwidth of just a few hundred hertz. This project aims to develop a sensor node 

for SHM using a guided wave ultrasonic technique with an ability to actuate the 
52

transducer and to acquire signals with a bandwidth up to 1 MHz. Those issues 

impose a challenge in wireless sensor networks because of the limited power 

sources, which are usually in the form of small batteries. Consequently, this limits 

the resources available for data acquisition, processing, and communication.

4.1.4 Experimental Methods

A prototype for data actuation and guided wave actuation using a wireless module 

was built and verified. As shown in Fig. 4.1, the sensor node hardware includes 

the following modules: 1) a data acquisition module, which should be designed to 

acquire data depending on the selected transducer; 2) a microcontroller that 

coordinates the behavior of all the components of the sensor node, stores acquired 

measurements, and carries simple computations locally; and 3) the RF interface 

for wireless communication. 

For actuation, it is desirable to use a narrowband excitation signal, which will 

minimize the effect of dispersion. The excitation signal that is most commonly 

used is the Hanning window tone burst signal. When a narrowband excitation 

signal is used, the sensed signal has a similar bandwidth, and the wave reflections 

from defects are determined by the peaks of the sensed signal envelope. The 

sensor node used in this work is the Iris wireless module (also called Iris mote), 

available from Memsic Corporation (www.memsic.com). The Iris mote hardware 

components and properties are summarized in Table 4.1. It operates on 2 AA 

batteries, and it is designed using hardware components with very low power 

consumption for maximizing the batteries’ lifetime. For data acquisition, the Iris 

mote has a 10-bit analog-to-digital converter, and the sampling frequency is 

limited to 273 ksps, which is not sufficient for sampling ultrasonic guided wave 

signals having a bandwidth up to 500 kHz. Moreover, the Iris mote does not have 

an actuation interface that is needed for guided wave excitation. However, it has a 

51-pin connector that makes peripheral interfaces of the microcontroller available 

for connection to external boards that could extend its functionality. This 

connector was used to implement an extension circuit board for signal 

conditioning that would allow the acquisition of narrowband ultrasonic signals 

with a central frequency up to 1 MHz from the piezoelectric transducer. The 

extension circuit board also provides an actuation interface that could convert a 

digital square signal into an analog tone burst narrowband signal for exciting the 

transducer. The extension circuit board is designed using off-the-shelf circuit 

components and operational amplifiers.
53

Table 4.1 Properties of the Iris mote sensing module, processing module, and RF module

Component Properties

Sensing module Resolution: 10 bits

Analog to digital convertor Sampling frequency: 273 ksps

Processing module 7.37-MHz processing clock

Digital I/O interface pins

Storage: 8-kB RAM

Flash memory: 512 kB

ATmega1281 μcontroller

Wireless communication module IEEE 802.14.5 compliant

Bit rate: 250 kbps 

Range: 50 m (indoor), 300 m (outdoor)

RF230 Zigbee radio

The extension circuit board connected to the Iris mote and the block diagram of 

its circuit components are shown in Fig. 4.2. The extension circuit board is 

provided its own power source using 4 AAA batteries. 

(a) (b)

Fig. 4.2 a) The extension circuit board connected to the Iris mote using the 51-pin 

connector. The sensor node has a total area less than 25 cm2

. b) The extension circuit board 

block diagram showing the sensing and actuation modules and the switch that selects which 

module will be connected to the PZT wafer.

4.1.5 Validation

The experimental setup shown in Fig. 4.3 was used to test the performance of the 

sensor nodes for active guided wave inspection. Two PZT wafers are bonded to 

an aluminum plate using epoxy, and each PZT wafer is connected to a sensor 

node. Sensor node 1 is used for actuation, and sensor node 2 is used for sensing. 

The outputs of the charge amplifier and the envelope detector of sensor node 2 are 

connected to the oscilloscope. The resulting sensed signal and its envelope 

recorded on the oscilloscope and the envelope data samples received at the base 

station are shown in Fig. 4.4. The envelope signals have been scaled to 

compensate for the sensing gain and the attenuation of the low-pass filter in the 

envelope detector. The charge amplifier output is time shifted to compensate for 

the group delay of the low-pass filter. The envelope detector has good sensitivity 

to the data variation and detects all the peaks of the wave packets. The 

interpolated digital signal samples transmitted wirelessly to the base station 

closely match the analog envelope signal from the oscilloscope.
54

Sensor node

2 Sensor node 

1

Fig. 4.3 Experimental setup for the validation of the sensing circuit. Two PZT wafers with 

dimensions 8 × 7 mm are bonded to the aluminum plate. Sensor node 1 is connected to PZT1 

and sensor node 2 is connected to PZT2. The outputs of the charge amplifier and the 

envelope detector of sensor node 2 are connected to the oscilloscope.

0 50 100 150 200 250 300 350

-1

-0.5

0

0.5

1

Time (μs)

Volts

Oscilloscope envelop

Oscilloscope charge amplifier

Interpolated digital samples

Digital samples

S0 mode

A0 mode

Fig. 4.4 The guided wave signal and its envelope as recorded on the oscilloscope are shown 

in green and blue, respectively. The sampled envelope data that is transmitted to the base 

station and their interpolation are also shown. The S0 and A0 modes incident wave packets 

are indicated. All the latter wave packets in the signal are due to reflections from the edges 

of the plate.

4.1.6 Finite Element Modeling

The finite element modeling software package Explicit/Abaqus is used to obtain 

the numerical data. In each simulation a 5-cycle Hanning window tone burst at 

100 kHz frequency actuation signal is applied to one transducer using the 

perfectly bonded piezoelectric actuator assumption. The results in Fig. 4.5 show 

the wave propagation in a single-layered and multilayered (8) composite plate.
55

 

Fig. 4.5 Lamb wave finite element method model in a single layer carbon fiber–reinforced 

polymer (CFRP) (left) and 8-layer quasi-isotropic CFRP laminate (right)

4.1.7 Signal Processing Algorithms for Damage Detection

Experimental measurements from PZT sensors mounted on aluminum and 

composite plates were processed successfully using signal processing algorithms. 

As a first step, the dispersion curves for 8-layer, 5-mm-thick woven composite 

plates using Lamb wave equation for anisotropic media and a transfer matrix 

method were obtained. A schematic of the test sample and the corresponding 

dispersion curves are presented in Fig. 4.6. An efficient 2-stage algorithm for 

near-real-time decomposition of Lamb waves into constitutive modes, which 

includes time-frequency analysis and matching pursuit, was then applied.

0 1 2 3 4 5 6 7 8

x 105

1000

1500

2000

2500

3000

3500

4000

4500

5000

5500

Dispersion Curves of 5 mm Composite Fiber Reinforced Plate

Frequency, Hz

Group velocity, m/s

Phase velocity, m/s

x3 x2

x1

θ

φ

Fig. 4.6 Schematic of multilayered composite material and phase velocity dispersion curves

Low variation of the group velocity at the inspection frequency (200 kHz) makes 

it possible to apply the standard diagnostic imaging algorithm to damage 

detection in woven composite materials. Diagnostic imaging was performed using 

the Lamb wave data from healthy and damaged samples. A-0 mode at 80 kHz was 

considered. The ellipses, which define the most possible damage location, were 

computed for sensor pairs that detected the presence of damage. The results of 

imaging impact damage on a composite plate and a surface-bonded stiffener are 

presented in Figs. 4.7 and 4.8.
56

450x630x5 mm composite 

Impact damage

Damage

Computed ellipses Most probable damage location

PZT-sensors

Fig. 4.7 Imaging of composite plate with impact damage

PZT-B

Metallic stiffener

PZT-A

PZT-E PZT-D

PZT-C

Computed ellipses Most probable 

damage location

Fig. 4.8 Imaging of 450- × 630- × 5-mm composite plate with surface-bonded aluminum 

stiffener to simulate surface damage

4.1.8 Outcomes

The results of the research were disseminated in the following publications:

G. Dib, R. Lattral, L. Udpa, G. Yang, “A wireless sensor node for structural 

health monitoring using guided wave inspection,” Materials Evaluation

(Fellowship paper), vol. 71, 2013.

G. Dib, R. Latteral, L. Udpa, “Wireless sensor networks for continuous health 

monitoring using ultrasonic inspection methods”, ASNT Fall Conference, 

Orlando, FL, 2012.
57

4.1.9 References

[1] Alleyne, D.N. and Cawley, P. (1992) “Optimization of Lamb wave 

inspection techniques”, NDT&E, pp. 11–22.

[2] Sirohi, J. and Chopra, I. (2000) “Fundamental Understanding of Piezoelectric 

Strain Sensors”, Journal of Intelligent Material Systems and Structures, vol. 

11.

[3] Akyildiz, I.F., Su, W., Sankarasubramaniam, Y. and Cayirci, E. (2002) 

“Wireless sensor networks: a survey”, Computer Networks, pp. 393–422.

[4] Lynch, J. and Loh, K.J. (2006) “A Summary Review of Wireless Sensors and 

Sensor Networks for Structural Health Monitoring”, The Shock and Vibration 

Digest, vol. 38, no. 2, pp. 91–1.
58

5. Thrust Area 3A: Structural Integrity of Composites 

5.1 A Model for Fatigue Life Prediction of Composite Structures

Xinran Xiao, Andrew Conway, and Arun Krishnan 

5.1.1 Summary

This chapter presents the effort on the development of a 3-dimensional finite 

element–based progressive fatigue damage module capable of lifetime prediction 

in fiber-reinforced composite materials. A progressive fatigue damage model 

(PFDM) [1,2] was selected for evaluation. The PFDM was implemented in 

ABAQUS through a user-defined material model. The model predictions were 

compared with fatigue experiments of AS4-3506-1 composite laminates with a 

central hole. The effort on generating PFDM parameters for an S-2 Glass/SC-15 

epoxy composite is reported.

5.1.2 Introduction

The overall objective of this thrust area is to develop the capability for the 

evaluation and prediction of fatigue life and durability for 3-dimensional (3-D)

composite components. The work started with a literature review. Much to our 

surprise, despite the availability of a great amount of fatigue test data and a 

fundamental understanding of fatigue damage mechanisms of composite 

materials, a general design tool that translates the knowledge and data generated 

with lab-scale coupons to structural applications did not exist. Three integrated 

composite fatigue prediction tools were reported in open literature—namely, the 

progressive fatigue damage model (PFDM) [1,2], MRLife [3], and MAE [4] 

models. However, none of the models has been evaluated by a second group 

besides the work of the original developers.

Among the 3 models, the PFDM is most closely aligned with the finite element 

analysis (FEA) widely used in structural design. The PFDM incorporates a set of 

empirical stiffness and strength degradation rules and master fatigue curves 

established from testing unidirectional composite into FEA. It allows the stress 

analysis of composite structures with arbitrary lay-up configurations and loading 

sequences with a gradually degraded property. The parameters for PFDM are 

available for the AS4-3506-1 composite [2]. The PFDM was selected for 

investigation.
59

To evaluate the PFDM, the model was implemented as a user material model in 

ABAQUS. Fatigue experiments were carried out with AS4-3506-1 composite 

plates with a central hole. The strain field evolution during fatigue experiment 

was recorded with digital image correlation (DIC) measurement. Our results 

showed that the PFDM was capable of predicting the strain redistribution due to 

fatigue damage in the 2 types of AS4-3506-1 laminates examined. This led to the 

suggestion to work on materials relevant to the sponsors. In response to this need, 

we worked on generating PFDM parameters for a plain weave S-glass/CS-15 

epoxy composite. The total number of fatigue tests required is 240. So far, about 

45% of the tests have been completed.

5.1.3 Fatigue Model Overview

Fatigue life prediction for composite structures lags far behind as compared to 

that for metallic or other structures. Metal fatigue is characterized by crack 

initiation and propagation. Fatigue life prediction often concerns the propagation 

of one major crack [5,6]. With fracture mechanics–based methodologies, one can 

predict the remaining life of components with cracks with high confidence. 

In comparison, fatigue damage mechanisms in composites are far more 

complicated. Composite materials are inhomogeneous at the micro and meso 

levels. The inhomogeneity and anisotropy mean that multiple damage modes, 

such as matrix cracking, fiber/matrix debonding, fiber fracture, fiber buckling, 

and delamination, may occur at different scales. These damage modes may grow 

at different rates and interact in a variety of ways under the influence of the local 

stress field. The fatigue damage process starts early in the life of a composite 

structure and is characterized by a reduction in the stiffness at damage zones. This 

leads to a continual redistribution of stress within the structure over time. 

A large body of literature on fatigue damage mechanisms in composites has been 

generated since the 1960s. Comprehensive reviews on various aspect of 

composite fatigue are available in the literature. A detailed mechanism-based

review on composite fatigue has been provided by Hahn [7]. The state-of-the-art 

of damage analysis for the prediction of structural integrity and durability of 

composite materials can be found in refs. 8–10. The effect of variable amplitude 

of loading on fatigue of composites has been investigated by Post et al. [11]. 

Despite the wide range of fatigue models, the generality of the approaches 

remains very limited. There is a gap between the complex distribution of stresses 

found within the load-bearing structures of civil and military vehicles and 

available fatigue models, which are often developed for a single material system 

under a specific loading, such as uniaxial tension/compression.
60

In the past decade, several research groups have attempted to incorporate fatigue 

models within the framework of a structural finite element simulation. Among the 

available integrated composite fatigue prediction tools [1–4], the PFDM 

developed by Shokrieh and Lessard [1,2] has proven to be the most general in 

terms of allowing variable lay-up configurations, loading conditions, and 

geometries to be modeled. However, the available model parameter is restricted to 

a given unidirectional material system, and delamination was modeled implicitly 

(i.e., there was no separation between the plies in the finite element model). 

5.1.4 PFDM and Its Implementation

Based on a modeling strategy originally developed by Adam et al. [16], the 

PFDM couples normalized models of fatigue life, residual stiffness, and residual 

strength [2,17] with standard FEA. Figure 5.1.1 provides a summary of the 

equations in the model and a flowchart for its implementation in ABAQUS userdefined material model (UMAT). 

 a) b)

Fig. 5.1.1 a) A flowchart for the implementation of the PFDM using an ABAQUS UMAT. 

b) The empirical equations used in PFDM in determining fatigue life and stiffness/strength 

degradation.

5.1.5 Experimental

The PFDM parameters are available for AS4/3501-6 [2] and, therefore, the 

evaluation was carried out with AS4/3501-6. The AS4/3501-6 carbon/epoxy 

laminates were manufactured from prepreg using the standard autoclave cure 
61

cycle at the University of Utah. Specimens were cut from the plates using a watercooled, diamond-tipped circular saw. A 10-mm-diameter central circular hole was 

machined using a carbide drill bit. 

Fatigue tests were conducted in the tension-tension regime with an R-ratio of 0 at 

10 Hz. Three types of laminates were tested: [02/902]s, [452/–452]s, and 

[0/90/0/90]s. DIC measurements were taken at prescribed fatigue cycle 

increments. Istra4D software by Dantec Dynamics was used for DIC analyses. 

5.1.6 Evaluation of PFDM

The evolutions of the strain fields predicted by simulations are compared side by

side with DIC measurements in Fig. 5.1.2a for [02/902]s at Pmax = 25 kN and 

R = 0. In these figures, the left half of the contours shows the results from the 

PFDM, while the right half presents the DIC results from the fatigue experiments. 

The predicted full-field strains agreed very well with the DIC measurements. In 

Fig. 5.1.2a, a black dotted vertical line near the root of the hole can be seen in the 

DIC measurement, which was caused by the loss of painted speckle pattern due to 

longitudinal splits at the specimen surface. Because of the loss of speckle pattern, 

a full-field DIC image was no longer available beyond N = 9000. Good 

correlations between the finite element prediction and DIC measurement were 

also observed in other load cases. Figure 5.1.2b compares the failure pattern 

between predictions and experiments. The agreement is reasonably good.

N=1000 N=5000 N=9000 N=1000 N=5000 N=9000 

(a)

(b) (c)

Fig. 5.1.2 Comparisons of PFDM prediction and experiments. a) PFDM vs. DIC: first 

principal strain (left) and shear strain (right) evolution with fatigue cycles on a [02/902]s

laminate at Pmax = 25 kN and R = 0. b) Failure patterns of [02/902]s (left) and [452/–452]s

(right) laminates. c) The displacement increases with the number of fatigue cycles, [02/902]s

at Pmax = 25 kN and R = 0. The prediction-matched experiment up to the onset of 

delamination. The 2 simulations are shown in red (with modified values of A and B [19]) and 

blue (with values of A and B [2]).
62

Table 5.1.1 compares the number of cycles to failure by PFDM prediction and 

fatigue experiments for 3 types of laminates. The predicted fatigue life in general 

is longer than the experimental value. As shown in Fig. 5.1.2c, PFDM predicted 

the change in specimen compliance closely up to N = 20,000. The rapid increase 

in compliance in experiment was due to delamination. The overprediction in 

simulation is likely due to the lack of explicit modeling of delamination in the 

current model.

Table 5.1.1 Fatigue experiments and PFDM predictions of AS4/3501-6 with center hole

In summary, the PFDM is capable of predicting the strain redistribution and the 

change in global stiffness in a laminated composite structure being subjected to 

fatigue load up to the point when damage progresses into delamination (i.e., into 

the fourth stage of the 5-stage fatigue damage development process established by 

Reifsnider and his co-workers [18]). Although the PFDM approach requires an 

extensive material characterization program, once the model parameters are 

established, the PFDM can be a great asset in evaluating various design options 

against fatigue failure. 

The PDFM has 2 major limitations: 1) Based entirely on data generated with a 

unidirectional laminate, the PFDM lacks the ability to model delamination in 

general laminates. Using the rapid increase in structural compliance as an 

indication of failure can improve the accuracy of fatigue failure prediction. To 

have a complete model for composite fatigue, it is necessary to include 

delamination. 2) The PFDM lacks of a proper algorithm to account for the effect 

of fatigue damage accumulation on cycle life under variable amplitude loading 

conditions (i.e., a cumulative damage rule). To this end, a novel degraded 

strength-based model for cumulative damage has been proposed [19]. 

5.1.7 Fatigue Testing with S-2 Glass/SC-15 Epoxy Composite

Plain weave S-glass/SC-15 epoxy composite panels of 24 × 12 × 0.2 inches were 

manufactured at the Composite Vehicle Research Center lab through a vacuumassisted resin transfer molding process. Table 5.1.2 presents the quasi-static 

properties of the manufactured composite. These properties are comparable with 

literature data.

Laminate R ratio Pmax (kN) Nf experiment Nf prediction

[02/902]s 0 15 >200000 >200000

[02/902]s 0 25 35000 80000

[452/-452]s 0 5 147500 >200000

[452/-452]s 0 6.5 1479 43000

[0/90/0/90]s 0 28 >275000 134000

Laminate R ratio Pmax (kN) Nf experiment Nf prediction

[02/902]s 0 15 >200000 >200000

[02/902]s 0 25 35000 80000

[452/-452]s 0 5 147500 >200000

[452/-452]s 0 6.5 1479 43000

[0/90/0/90]s 0 28 >275000 134000
63

Table 5.1.2 Mechanical properties of the manufactured S-glass/SC-15 composite

Test Modulus

(GPa)

Strength

(MPa) Test Method

Tensile 24.3 ± 1.1 477.9 ± 19.3 ASTM D3039

Compression . . . 280.1 ± 27.8 ASTM D3410 modified

In-plane shear 2.24 59.0 ± 3.0 ASTM D7078

Assume the woven composite being in-plane isotropic, a total of 240 fatigue 

experiments are needed to establish the empirical relationships in the PFDM, as 

detailed in Table 5.1.3. 

Table 5.1.3 Test matrix for PFDM model parameters for a woven composite

Loading Specimen Testing 

standard

Residual 

stiffness/strength

S-N 

curve

Tension ASTM 

D3039/D3479

30 30

Compression ASTM D3410 

Modified

30 30

In-plane 

shear

ASTM D7078 30 30

Out-of-plane 

shear

ASTM D3846 

modified

30 30

total 120 120

Note: Green cells = completed fatigue tests required for PFDM for S-glass/CS-15 epoxy composite.

The data under uniaxial loading have been generated at 4 different R-ratios: 

R = 0.4, 0, –1, and 10. At each R-ratio at least 3 stress amplitude values were 

chosen. A minimum of 3 specimens were tested in fatigue at each value of stress 

amplitude. Figure 5.1.3a presents the master fatigue curves with R = 0.7, 0.4, 0, 

–1, and 10. The results clearly indicate that the master curve under uniaxial 

loading needs to be separated into 2 curves: one for tensile-tension region and the 

other for tension-compression and compression-compression [21]. Figure 5.1.3b

presents in-plane shear fatigue SN curves that have been measured with R = 0, 

0.2, and 0.4.
64

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

0 1 2 3 4 5 6

Master curve life parameter (u)

log (Number of Cycle to Failure)

R=-1 R=0

R=0.4 R=10

R=0.7

R = 0, 0.4, 0.7

u = 0.6494 + 0.5692*log(Nf)

R = -1, 10

u = 1.2146 + 0.2023*log(Nf) 

0

5

10

15

20

25

30

35

0.0 1.0 2.0 3.0 4.0 5.0 6.0

Number of Cylces to Failure Log(Nf)

Stress Amplitude (MPa)

R=0

R=0.2

R=0.4

 a) b)

Fig. 5.1.3 S-2 Glass/SC-15 epoxy composite fatigue data. a) Master fatigue curve for 

uniaxial loading. b) In-plane shear with R-ratio of R = 0, 0.2, and 0.4.

The gradual degradation under tensile fatigue with R = 0 has been measured with 

specimens fatigued till 10%, 20%, 40%, and 60% of the fatigue life with a stress 

amplitude at 60%, 70%, or 80%. The results are presented in Fig. 5.1.4a and b for 

the tensile module and strength, respectively. Additional experiments are needed 

to extend the data for 80% of the fatigue life. The gradual degradation experiment 

under in-plane shear loading was just about to start when the funding for this 

project ended in August 2013. 

In summary, about 45% of the fatigue tests required for PFDM for S-glass/CS-15 

epoxy composite have been completed (shown in Table 5.1.3 in the cell shaded in 

green). Most of the fatigue tests were carried out by 4 undergrad students: 

Thomas Stevenson (2012–13), Eric Rightor (2012–13), Gabrielle Colby (2013), 

and Garrett Dunn (2013).

0.0

0.2

0.4

0.6

0.8

1.0

0 0.2 0.4 0.6 0.8

N/Nf

Normalised 

Residual Modulues 

60%

70%

80%

 

0.0

0.2

0.4

0.6

0.8

1.0

0 0.2 0.4 0.6 0.8

N/Nf

Normalized 

Residual Strength 

60%

70%

80%

 a) b)

Fig. 5.1.4 Gradual degradation of a) the tensile modulus and b) tensile strength of S-2 

Glass/SC-15 epoxy composite measured at R = 0
65

5.1.8 References

[1] Shokrieh, M.M. and L.B. Lessard, “Progressive Fatigue Damage Modeling 

of Composite Materials, Part I: Modeling,” Journal of Composite Materials, 

2000, 34(13), p. 1056–1080.

[2] Shokrieh, M.M. and L.B. Lessard, “Progressive Fatigue Damage Modeling

of Composite Materials, Part II: Material Characterization and Model 

Verification,” Journal of Composite Materials, 2000, 34(13), p. 1081–1116.

[3] Lesko, J.J. and S.W. Case, “Multifunctional Composites for Next Navy 

Seaframes,” Overall Project Final Report, 2009.

[4] Sihn, S. and J.W. Park, “MAE: An Integrated Design Tool for Failure and 

Life Prediction of Composites,” Journal of Composite Materials, 2008, 

42(18), p. 1967–1988.

[5] RW. Hertzberg, Deformation and Fracture Mechanics of Engineering 

Materials, 4th edition, John Wiley & Sons, Inc, New York, 1996.

[6] R.J. Sanford, Principles of Fracture Mechanics, Prentice Hall, Upper 

Saddle River, NJ, 2003.

[7] Hahn T., Fatigue of Composites, Delaware Composites Design 

Encyclopedia, Vol.1, Eds C Zweben, T. Hahn, TW Chou, Technomic 

Publishing Co. Inc, Lancaster, PA, 1989.

[8] Talreja R, “Damage analysis for structural integrity and durability of 

composite materials,” Fatigue & Fracture of Engineering Materials and 

Structures, 29 (7), 481–506, 2006.

[9] Reifsnider KL, Case SW, Damage Tolerance and Durability of Materials 

Systems, Wiley-Interscience, New York, 2002. 

[10] Reifsnider KL, Damage and Damage Mechanics, in Fatigue of Composite 

Materials, ed. Reifsnider KL, Elsevier, New York, 1991. 

[11] Post NL, S.W. Case, J.J. Lesko, “Modeling the variable amplitude fatigue of 

composite materials: A review and evaluation of the state of the art for 

spectrum loading,” International Journal of Fatigue, 30 (2008) 2064–2086. 

[12] Conway AR, Xiao X, “Finite Element Based Fatigue Modeling of Fiber 

Reinforced Composite Materials: A State-of-the-art Review,” MSU Final 

Report for TACOM Cooperative Agreement Number W56HZV-07-2-0001, 

19 May 2010.
66

[13] Allen, D.H., Damage Evolution in Laminates, in Damage Mechanics of 

Composite Materials, R. Talreja, Editor. 1994, Elsevier, New York.

[14] Talreja, R., Damage Mechanics of Composite Materials. 1994, New York, 

Elsevier.

[15] Tserpes, K.I., et al., “Fatigue Damage Accumulation and Residual Strength 

Assessment of CFRP Laminates,” Composite Structures, 2004, 63(2), p. 

219–230.

[16] Adam T, Fernando G., Dickson R.F., Reiter H., Harris B., Fatigue Life 

Prediction for Hybrid Composites. International Journal of Fatigue, 1989, 

11(4), p. 233–237.

[17] Conway A., Xiao X., “Progressive Fatigue Damage Modeling in CFRP 

Laminates using an ABAQUS UMAT Approach,” 26th ASC/Second joint 

ASC-CACSMA conference on composites, September 26–28, 2011.

[18] Reifsnider, K.L., Damage and Damage Mechanics, in Fatigue of Composite 

Materials, Elsevier, New York, 1991.

[19] Christopher Cater, Xinran Xiao, Arun Krishnan,” Cumulative Fatigue 

Damage Prediction of Composite Structures,” Proceeding of ICCM19, 

Montreal, July, 2013

[20] Shokrieh, M.M. “Progressive Fatigue Damage Modeling of Composite 

Materials,” Thesis, 1996.

[21] Xinran Xiao, Arun Krishnan, Thomas Stevenson, “Master Fatigue Curve 

For A Plain Weave Glass/Epoxy Composite,” DURACOSYS 2012, Brussels, 

Belgium, September 2012.

5.1.9 Publications

[1] A. R. Conway, X. Xiao, “Implementing a Finite Element Module for Fatigue 

Damage Modeling in Fiber Reinforced Composite Materials,” American 

Society for Composites 25th Technical Conference, September 20–22, 2010.

[2] Conway A., Xiao X., “Progressive Fatigue Damage Modeling in CFRP 

Laminates using an ABAQUS UMAT Approach,” 26th ASC/Second joint 

ASC-CACSMA conference on composites, September 26–28, 2011.

[3] Krishnan A., Conway A., Xiao X., “Validation of a Progressive Fatigue 

Damage Modeling for composite laminates using Digital Imaging 

Correlation,” Proceedings of the American Society for Composites 27th 

Technical Conference, Fort Worth, Dallas, September 2012.
67

[4] Xinran Xiao, Arun Krishnan, Thomas Stevenson, “Master Fatigue Curve for 

A Plain Weave Glass/Epoxy Composite,” DURACOSYS 2012, Brussels, 

Belgium, September 2012.

[5] Christopher Cater, Xinran Xiao, Arun Krishnan, “Cumulative Fatigue 

Damage Prediction of Composite Structures,” Proceeding of ICCM19,

Montreal, July, 2013.
68

5.2 Multiscale Damage Modeling

Christopher Cater and Xinran Xiao 

5.2.1 Summary

This paper presents the development of a multiscale method for composite 

damage modeling, particularly at the free edge. The free-edge effect has been 

studied extensively at the mesoscale. In this work, it was explored at the scale of 

fiber and matrix with a top-down one-way coupling multiscale approach. 

5.2.2 Introduction

Fiber-reinforced polymer (FRP) composites are hierarchical in nature, containing 

a variety of length scales that factor into the overall properties of the composite 

[1]. The various length scales associated with FRP composites can be classified as 

shown in Fig. 5.2.1.

Fig. 5.2.1 Diagram of the various length scales associated with a composite structure

The microscale constituents, and their relative volume fractions, could be adjusted 

along with a chosen reinforcement architecture (laminated, woven, braided) to 

obtain a composite best fit for the intended application. A consequence of the 

hierarchical nature of FRP, however, is an increased complexity, particularly in 

modeling the damage and failure due to a variety of mechanisms evolving at several 

length scales [2,3]. A multiscale approach is required to address the problem. 

The objective of this work is to investigate the effect of microstructural 

parameters on the initiation of material failure at the free edge. Such parameters 

include the effect of microstructure (fiber/matrix properties, fiber volume 

fraction) on the potential sources of failure initiation (fiber/matrix separation, 

matrix cracking, etc.). 

5.2.3 Literature Review

The free-edge stresses have long been studied in available literature. These span 

from approximate closed-form solutions [4–6] to 2-dimensional (2-D) generalized 

plane strain analysis using finite elements [7,8] and to advanced higher-order 

generalized laminate theories to capture the interlaminar stresses in a 2-D domain 

space [9–17]. A more comprehensive review can be found by Mittelstedt and 
69

Becker [18]. Recent work has focused on developing 3-dimensional (3-D) models 

of the laminate free-edge problem utilizing submodeling techniques [19] or new 

finite element approaches [20]. While the previous research has been successful in 

characterizing the nature of the free-edge stresses with respect to changes in 

lamina orientation, geometry, and loading conditions, these analyses have been 

restricted to the mesoscale domain.

Multiscale investigations on free-edge stresses have started to appear in literature. 

Dustin and Pipes [21] compared the stress singularity associated with lamina 

interfaces at the free edge (mesoscale) to that associated with fiber termination at 

the free edge (microscale). Fiber/matrix interface cracking was also found to be 

an initiating damage mechanism of interply cracking in [+15/–15]S laminates 

[22]. Other works include numerical simulations of free-edge stresses in singlefiber finite element models under simplified transverse tensile loading and 

uniform temperature change [23]. Multifiber models have also been investigated 

near a free edge by using domain decomposition [24], a superposition method 

[25], and dehomogenization methods [26–28]. These recent works have 

highlighted the stresses at the microscale (fiber/matrix) near the free edge and the 

criticality of defects; yet, they are still unable to address the following research 

questions: 1) What are the microscale stresses at various locations in the lamina 

and how do they influence damage initiation/evolution? 2) What is the coupled 

relation between mesoscale parameters (lamina thickness/orientation) and 

microscale properties (matrix properties, fiber volume fraction, fiber/matrix 

interface) in relation to initial laminate failure?

5.2.4 Approach

This work revisits the problem of free-edge effects in composite laminates using a 

combined multiscale modeling and computational micromechanics approach. A 

schematic of the proposed approach is illustrated in Fig. 5.2.2 for a laminate under 

uniaxial tension. At the macroscale, the material is assumed to be orthotropic and 

homogeneous at lamina level, and will experience a uniform displacement in the 

loading direction. This extensional displacement is prescribed to the mesoscale in 

the z-direction. At the mesoscale, a submodel is used to capture the free-edge 

stresses within the laminate. Each lamina is modeled as homogeneous and 

orthotropic with multiple layers of elements through its thickness. Using a semiconcurrent multiscale scheme, the mesoscale is linked to a microscale through the 

use of a representative volume element (RVE), which captures the local 

heterogeneity of the fiber/matrix constituents. Periodicity will be employed at 
70

both the mesoscale and microscale models in the loading (z) direction (to allow 

for the analysis of off-axis laminates), and a free edge will explicitly be captured 

at the right, or x-, boundary. 

Fig. 5.2.2 Proposed multiscale workflow for the free-edge analysis of a laminated 

composite, including the effects of the local microstructure

The boundary conditions for the free-edge analysis at the microscale are given in 

Fig. 5.2.3 [29]. The mesoscale strains at regions of interest are used to provide the 

necessary displacement boundary conditions for the free-edge microscale 

analysis.

a) b)

Fig. 5.2.3 a) The finite element mesh of the microscale RVE and b) the applied boundary 

conditions for the free-edge microscale analysis

5.2.5 Results and Discussions

The 2 composite laminates investigated were [25/–25/90]S, and [02/90]S. For each 

laminate, 2 specific regions of interest were investigated: 1) the interlaminar 

interface between the –25/90 or 0/90 plies (interface micromodel) and 2) the 

midplane of the laminate (midplane micromodel). 

5.2.5.1 [25/-25/90]S Laminate

The through-thickness, or (σy), stress component from the mesoscale model is 

shown in Fig. 5.2.4. Figure 5.2.4a shows a region near the free edge, where the 2 

elements used for extracting boundary conditions for the midplane and interface 
71

micromodels are indicated. The strain components at the mesoscale integration 

points are presented in Fig. 5.2.4b, where the logarithmic strain presented in the 

1-2-3 coordinate system is analogous to the x-y-z. The most notable difference 

between the 2 locations is the presence of large interlaminar shear strains, 

particularly in the 2-3 (y-z) plane at the interface, caused by the mismatch in 

extensional-shear coupling between the –25° and 90° plies.

Fig. 5.2.4 a) Y-stress contour in the [25/–25/90]S laminate under uniaxial tension (units are 

in megapascals). b) The strains extracted from the interface and midplane elements.

Figure 5.2.5 shows the matrix Tresca and first principal stress contours at the free 

edge (X–

) obtained with the micromodels. The predicted max shear stress at the 

interface was 33% higher than that found at the midplane. Additionally, the 

locations of highest max shear stress varied. The midplane micromodel had max 

shear stresses between fibers in the macro-loading (z) direction. The interface 

micromodel, on the other hand, showed high max shear stresses in locations both 

between proximal fibers as well as at an angle from the z-axis. The difference in 

high shear stress regions in the 2 micromodels was likely due to the increased 

shear strain (mesoscale) closer to the interlaminar interface.

Midplane, 1st Interface, 1 Principal st Interface, Max Shear Midplane, Max Shear Principal

Fig. 5.2.5 Contours of the max shear stress and the first principal stress at the free edge 

(X–

) generated in the interface and the midplane micromodels in a [25/–25/90]s laminate. 

(Units are in pascals.)

The first principal stress was 35% higher at the interface as compared to the 

midplane. For both locations, the regions of highest first principal stress at the 

free edge coincided with those containing the highest Tresca stresses, near the 
72

fiber/matrix boundary. The direction of the first principal stress in these regions 

was normal to the fibers, indicating a potential source of opening (fiber/matrix 

debonding) failure. 

The cohesive stress between the fiber and matrix was extracted from the 

micromodels. Figure 5.2.6 (a–c) plots the normal (τN), radial shear (τR), and axial 

shear (τA) stresses along the fiber length at the location that had the highest stress 

normalized to the matrix tensile strength (σt). As shown, a 20% higher maximum 

normal stress between the fiber/matrix is found at the interface as opposed to the 

midplane. The radial stress exhibited a singularity as the free edge was reached. 

The regions of highest radial shear stress between the fiber and matrix for both 

models were collocated with the max Tresca stress in the matrix. The max axial 

shear stress, Fig. 5.2.6c, was the only fiber/matrix stress component to be higher 

at the midplane than the interface. The axial shear component, however, vanishes 

to zero on the traction free edge, limiting its importance in fiber/matrix debonding 

at the free edge. Based on the results from the normal and shear stresses between 

the fiber and matrix, fiber/matrix debonding at free edge is likely to occur as a 

mixed-mode fracture caused by the local fiber/matrix material mismatch rather 

than driven solely by the macroscopic loading.

a) b)

c) d)

Fig. 5.2.6 a) Normal, b) radial shear, and c) axial shear tractions for midplane and interface 

elements along the fiber/matrix interface at the region of maximum value. The results are 

normalized with regard to the matrix strength, σt, and the distance to the free edge in the 

X- direction. d) Orientation of the cohesive traction directions.
73

5.2.5.2 [02/90]S Laminate

Figure 5.2.7a presents an excerpt of the free-edge stress contours for the normal, 

or y-stress, component. The mesoscale strains extracted from the interface and 

midplane elements are shown in Fig. 5.2.7b. Unlike the [25/–25/90]S laminate, the 

[02/90]S shows minimal shearing (zero for the y-z and x-z shear directions) and an 

8% increase in the y-strain (shown as LE2).

Fig. 5.2.7 a) Y-stress contours for the [02/90]S laminate and b) the mesoscale strains 

extracted from the interface and midplane elements

The microscale Tresca stress contours for the interface and midplane micromodels 

in the [02/90]S laminate match both qualitatively and quantitatively to those of the 

midplane results for the [25/–25/90]S laminate. Thus, these matrix shear stresses 

are a function of the material inhomogeneity, local fiber/matrix free-edge effects, 

and the macroscopic displacement (in the z direction) alone and not lamina-level 

shear stresses. In all cases, the matrix shear stress in the cross-ply laminate was 

less than those of the interface element micromodel for the [25/–25/90]S.

The maximum principal stresses at the microscale in the [02/90]S laminate was 

about 14.7% higher than that in [25/–25/90]S laminate for their respective 

midplane regions (the angle ply laminate had higher values at the interface). The 

highest stress was found to align with the macroscopic loading (z-direction) 

between adjacent fibers and coincided location of highest high-fiber/matrix 

interfacial stresses. 

5.2.6 Future Work

The proposed multiscale approach allows the investigation of the microstructure 

at the free edge in composite laminates. This approach will improve the 

understanding of the coupling between mesoscale characteristics, such as laminate 
74

thickness and orientation, and the microscale characteristics, such as fiber and 

matrix properties, fiber volume fraction, and packing patterns, as well as address 

the complexities of material nonlinearity and residual stresses. 

5.2.7 References

[1] Degrieck J, Van Paepegem “W. Fatigue damage modeling of fibrereinforced composite materials: Review,” Appl Mech Rev 2001;54:279.

[2] Pineda EJ, Waas AM, Bednarcyk BA, Collier CS, Yarrington PW. “A 

Multiscale Progressive Damage and Failure Modeling Approach for 

Laminated Fiber Reinforced Composites,” Adv Math Model Exp Methods 

Mater Struct 2010:43–56.

[3] Talreja R. “Damage analysis for structural integrity and durability of 

composite materials,” Fatigue Fract Eng Mater Struct 2006;29:481–506.

[4] Pagano NJ, Pipes RB. "Some observations on the interlaminar strength of 

composite laminates," Int J Mech Sci 1973;15:679–92.

[5] Pipes RB, Goodsell J, Ritchey A, Dustin J, Gosse J. "Interlaminar stresses 

in composite laminates: Thermoelastic deformation," Compos Sci Technol 

2010;70:1605–11.

[6] Goodsell J, Pagano NJ, Kravchenko O, Pipes RB. "Interlaminar stresses in 

composite laminates subjected to anticlastic bending deformation," J Appl 

Mech Trans ASME 2013;80.

[7] Wang ASD, Crossman FW. "Some New Results on Edge Effect in 

Symmetric Composite Laminates," J Compos Mater 1977;11:92–106.

[8] Raju IS, Crews Jr JH. "Interlaminar Stress Singularities at a Straight Free 

Edge in Composite Laminates," DTIC Document; 1980.

[9] Robbins, JR. DH, Reddy JN. "Variable Kinematic Modelling of Laminated 

Composite Plates,"pdf. Int J Numer Methods Eng 1996;39:2238–317.

[10] Nguyen V-T, Caron J-F. "A new finite element for free edge effect analysis 

in laminated composites," Comput Struct 2006;84:1538–46.

[11] Nguyen V-T, Caron J-F. "Finite element analysis of free-edge stresses in 

composite laminates under mechanical an thermal loading," Compos Sci 

Technol 2009;69:40–9.
75

[12] Lo SH, Zhen W, Cheung YK, Wanji C. "An enhanced global–local higherorder theory for the free edge effect in laminates," Compos Struct

2007;81:499–510.

[13] Carreira RP, Caron J-F, Diaz Diaz A. "Model of multilayered materials for

interface stresses estimation and validation by finite element calculations,"

Mech Mater 2002;34:217–30.

[14] Diaz A, Caron J-F, Pedro Carreira R. "Software application for evaluating 

interfacial stresses in inelastic symmetrical laminates with free edges,"

Compos Struct 2002;58:195–208.

[15] Tahani M, Nosier A. "Free edge stress analysis of general cross-ply 

composite laminates under extension and thermal loading," Compos Struct 

2003;60:91–103.

[16] Nosier A, Bahrami A. "Free-edge stresses in antisymmetric angle-ply 

laminates in extension and torsion," Int J Solids Struct 2006;43:6800–16.

[17] Nosier A, Maleki M. "Free-edge stresses in general composite laminates,"

Int J Mech Sci 2008;50:1435–47.

[18] Mittelstedt C, Becker W. "Free-edge effects in composite laminates," Appl 

Mech Rev 2007;60:217–45.

[19] Romera JM, Cantera MA, Adarraga I, Mujika F. "Application of the 

submodeling technique to the analysis of the edge effects of composite 

laminates," J Reinf Plast Compos 2013;32:1099–111.

[20] Esquej R, Castejon L, Lizaranzu M, Carrera M, Miravete A, Miralbes R. "A 

new finite element approach applied to the free edge effect on composite 

materials," Compos Struct 2013;98:121–9.

[22] Lecomte-Grosbras P, Paluch B, Brieu M. "Characterization of free edge 

effects: Influence of mechanical properties, microstructure and structure 

effects," J Compos Mater 2013;47:2823–34.

[21] Dustin JS, Byron Pipes R. "Free-edge singularities meet the microstructure: 

Important considerations," Compos Sci Technol 2012;72:933–7.

[23] Andrews EW, Garnich MR. "Stresses around fiber ends at free and 

embedded ply edges," Compos Sci Technol 2008;68:3352–7.

[24] Raghavan P, Moorthy S, Ghosh S, Pagano NJ. "Revisiting the composite 

laminate problem with an adaptive multi-level computational model,"

Compos Sci Technol 2001;61:1017–40.
76

[25] Fish J, Wagiman A. "Multiscale finite element method for a locally 

nonperiodic heterogeneous medium," Comput Mech 1993;12:164–80.

[26] Yan CK. On homogenization and de-homogenization of composite 

materials. Drexel University, 2003.

[27] Chung S. Effects of interlaminar stress gradients on free edge delamination 

in composite laminates. Drexel University, 2003.

[28] Dustin JS, Pipes RB. Multi-Scale Modeling of Free-Edge Micro-Cracks 

with XFEM, American Institute of Aeronautics and Astronautics; 2014.

[29] Christopher Cater, Xinran Xiao, "Multiscale Investigation of Free Edge 

Effects in Laminated Composites," Proceedings of the American Society for 

Composites 29th Technical Conference, San Diego, CA, September, 2014.

5.2.8 Publications

[1] Christopher Cater, Xinran Xiao, “Multiscale Investigation of Free Edge 

Effects in Laminated Composites,” Proceedings of the American Society for 

Composites 29th Technical Conference, San Diego, CA, September, 2014.

[2] Christopher Cater, Xinran Xiao, “Energy based multiscale modeling with 

non-periodic boundary conditions,” Proceedings of the American Society for 

Composites 28th Technical Conference, College Station, PA, September, 

2013.

[3] Christopher Cater, Xinran Xiao, “Boundary conditions for the multiscale 

analysis of composites featuring local RVE damage,” Proceedings of the 

American Society for Composites 27th Technical Conference, Arlington, TX, 

October 2012.
77

6. Thrust 3B: Multifunctional Composite Materials

Lawrence T Drzal

6.1 Summary

While investigating multifunctional composites, we have conducted research 

toward utilizing easy-to-manufacture, low-cost carbon nanoparticles to modify the 

material behavior of composites of interest to the US Army and Marines. 

The objectives of this work are as follows: 

• To demonstrate the ability to improve/control composite fracture and 

impact toughness by incorporating distributed and layered nanoparticles 

with functionalized elastomeric coatings.

• To define the multifunctionality gained by incorporating exfoliated 

graphite nanoparticles (xGnPs) into fiber-reinforced polymer (FRP).

• To transfer knowledge to other thrust areas to stimulate cross‐disciplinary 

advances and to disseminate results externally. 

In order to achieve the objectives of this project, 4 parallel material research 

efforts were undertaken. 

• First, we have focused on the functionalization of the surface of the xGnP 

particles to allow them to be dispersed in various vinyl ester and epoxy 

resins. By manipulating the surface treatments and coatings, we can 

increase the toughness, strength, and blast resistance of these same resin 

systems. 

• Second, additional properties can be enhanced by incorporating GnP and 

controlling its orientation and concentration. These properties include 

electrical conductivity, thermal conductivity, and barrier properties that 

can be enhanced by adding only a few percent of these particles to 

polymers. 

• Third, the application of the GnP to the surface of conventional carbonreinforcing fibers with a suitable fugitive sizing has been shown to 

improve the adhesion, transverse properties, and interfacial shear strength. 

• Fourth, insertion of GnP to the interlaminar region of the composite as a 

thin film (paper) can be used to control and direct energy absorption under 
78

blast and impact conditions, reducing the amount of structural damage to 

the composite. 

These findings are detailed in the following subsections.

6.2 Functionalization of GnP

Graphene nanoplatelet particles (GnPs) are produced by a top-down approach 

using an intercalation and exfoliation approach. The resulting GnP consists of a 

few layers (~7–15) of graphene with a small concentration (~2%–3%) of oxygen 

groups located at the GnP edges (Fig. 6.1). The basal surfaces of GnP are 

hydrophobic, low-energy surfaces with no evidence of disruption of the graphene 

lattice. The edge groups are not optimized for interaction with the various 

thermoset resin chemistries of interest to the Army and Marines and require 

functionalization for achieving the best mechanical properties.

Fig. 6.1 Edge groups that can be attached an xGnP particle

6.2.1 Elastomeric Functionalization of GnP

We attached carboxy-terminated poly butadiene co-acrylonitrile (CTBN) rubber 

to the edges of GnP using Hypro CTBNX 1300-13 rubber (Hycar, USA) at 

concentrations ranging from 0.5 to 10 wt%. Composite specimens with various 

amounts of CTBN-functionalized GnP were mixed into a vinyl ester matrix 

(Derakane 411-350 cured with methyl ethyl ketone peroxide using a cobalt 

octoate catalyst). With the addition of pristine GnP, the flexural modulus 

increased as much as 45%, and the modulus still was 36% higher with 5 wt% 

CTBN–GnP. The flexural strength decreased over the base vinyl ester but only by 

31%. More importantly, the un-notched Izod impact strength of 3% xGnP-gEpoxy-Amine-GMA reinforced the 510A-40 vinyl ester nanocomposites 

increased about 230%, compared to neat vinyl ester and 3% xGnP reinforced 

vinyl ester nanocomposite (Fig. 6.2). The result strongly depended on the CTBN 

coating concentration and the CTBN-coated xGnP loading. Optimum results were 

achieved with 3% xGnP with 10 wt% CTBN. The notched Izod impact tests did 
79

show the beneficial effect of elastomeric coating of the GnP nanoparticles. The

coating of CTBN onto GnP particles was successfully scaled up to produce large 

batches (~75 g).

Fig. 6.2 Impact strength of xGnP vinyl ester composites

6.2.2 Edge Modification of GnP

Functionalization of xGnP basal plane as well as the edges via the covalent 

interaction of sugar azide with C=C of xGnP was investigated to increase the 

number of sites. Covalent functionalization of graphene nanoparticles (GnPs) was 

conducted, employing 2,3,4-Tri-O-acetyl-β-D-xylopyranosyl azide followed by 

fabrication of an epoxy/functionalized graphene nanocomposite (Fig. 6.3). 

Successful functionalization of GnP was confirmed via thermogravimetric 

analysis and X-ray photoelectron spectroscopy. Raman spectroscopy indicated 

that the functionalization was on the edge of graphene sheets, as the basal plane 

was not perturbed as a result of the functionalization. An epoxy/functionalized 

GnP composite system produced an increase in the flexural modulus (~18%) and 

glass transition temperature (~10 °C) compared to an unfunctionalized GnP-based 

epoxy composite.

Fig. 6.3 Edge modification of an xGn particle
80

6.2.3 Edge Stitching of GnP

GnP is subject to shear separation of the graphene sheets under mechanical 

loading. In order to achieve better properties, it would be quite beneficial to tie the 

adjacent graphene layers together so they do not shear apart layer by layer in a 

process called “edge stitching” (Fig. 6.4). The edge-stitched GnP can reinforce the 

epoxy matrix in a superior fashion compared to unmodified xGnP. 

Functionalization of GnP edges was investigated via the covalent interaction of 

diamine with the COOH group of GnP at the edges. Conversion of COOH group 

at the edges to COCl groups improves the reactivity; subsequent covalent 

functionalization of the COCl groups at the edges can be accomplished with 

diamine. An edge-stitched functionalized xGnP stack acts as a robust reinforcing 

filler as slippage between layers will be minimized. Epoxy composites made with 

1% edge-stitched GnP produced a 58% increase in fracture energy as exhibited by 

the increase in strain-to-failure and increased area under the tensile stress-strain 

curve.

Fig. 6.4 Edge stitching of xGn particles

6.3 Coating of Carbon Fibers with GnP

The use of nanoparticles offered a very effective way to modify fiber-reinforced 

composites without altering the fiber-matrix composition. A problem occurs when 

a resin modified with nanoparticles is infused into the fiber preform. Filtering of 

nanoparticles takes place at the periphery of the composites as a result of the high 

tortuosity produced by the fibers and their small interfiber spacing. To be 

effective, the nanoparticles should be located in between the reinforcing fibers 

and near, but not on, the fiber surface. In a process developed at Michigan State 

University, a fugitive sizing can be used to coat the carbon fibers with the 

appropriate amount of nanoparticles prior to composite fabrication. A sizing has 

been formulated using a slightly cross-linked epoxy as the sizing vehicle in which 

GnP is suspended and applied to the fibers. The sizing is designed so that it swells 

and dissolves after impregnation and before vitrification, allowing the GnP to be 

confined to the fiber-matrix interphase. The single-fiber interfacial shear strength 
81

test was used to evaluate the effectiveness of the GnP particles. It was found that 

3 wt% of GnP in a 4 wt% sizing increased the carbon fiber-epoxy interfacial shear 

strength by 55% (Fig. 6.5).

Fig. 6.5 Single-fiber interfacial shear strength for different GnP loadings

6.4 Fabrication of GnP “Paper” Films

Besides the coating of individual GnP particles onto the fiber surface, a goal was 

to produce a GnP film that could be easily applied between composite lamina. 

This would allow control of the interlaminar strength under static and impact 

conditions. A simple process was developed for producing GnP “paper” in 

thicknesses from 3 to 30 μm, with various xGnP sizes 1, 15, and 50 μm and 

coupled with and without a reactive elastomeric interlayer. The procedure consists 

of dissolving polyethyleneimine (PEI) in water followed by the addition of the 

requisite amount of GnP. A ratio of GnP:PEI:water of 1:0.5:1000 was found to 

give the best results. The PEI is a cationic polyelectrolyte that binds to the GnP 

and also is attracted to the water, creating a robust suspension. The suspension is 

slowly filtered on a micron-sized filter and then dried and pressed followed by a 

300 °C heat treatment to decompose the PEI. The resulting “paper”, Fig. 6.6, is 

self-supporting with a high degree of GnP alignment in the plane. Samples as 

large as 30 × 30 cm have been fabricated. The resulting GnP “paper” has an 

electrical conductivity of 6*105 S/cm (0.1 ohm/sq), which is about 1.5 orders of 
82

magnitude less than copper—a density of approximately 1.6, which is 

approximately 15% that of copper, and a thermal conductivity of approximately

200 W/mK (in-plane) and 5 W/mK (through thickness), making it also a 

lightweight substitute for copper.

Fig. 6.6 XGnP papers

6.5 Interlaminar Toughening of Multilayer Vinyl Ester 

Composites Using GnP Paper

6.5.1 Moderate-Speed Impact Testing

Multilayer composite laminates using Shield Strand balanced weave fabric (Owens 

Corning S glass, 0.025 inch thick) and vinyl ester matrix (DER 411-350) from 

Ashland Specialty Chemical with 2-butanone peroxide (Luperox) and cobalt octotate 

accelerator were fabricated in 5- to 10-layer samples, 15 × 15 cm (Fig. 6.7). Lowspeed impact testing was conducted at a speed of 12 m/s using a 50-mm tup.

Fig. 6.7 Three types of laminates studied

Pristine or CTBN-coated GnP-5 μm, 25 μm, and hybrid 5/25 μm (40/60 wt%) 

papers were successfully made in a larger size (12 × 12 inches). Compressing the 

wet CTBN-modified xGnP paper at approximately 415 psi is necessary to produce 

an intact paper. The thickness of the CTBN-coated GnP paper can be controlled 
83

from 50 to 400 μm by adjusting the GnP loading or suspension amount, but thinner 

papers will require alternative methods. GnP paper in thicknesses from 100 to 

150 μm was inserted between layers of the laminate. Six-millimeter-diameter holes 

were inserted in the xGnP paper to allow resin flow to take place between lamina. 

The vinyl ester matrix concentration was important for mechanical properties, while 

the GnP concentration was more important for electrical properties. The composite 

with the greater fraction of holes showed higher flexural strength and modulus 

compared to other investigated composites. Among the CTBN coated xGnP paper 

reinforced multilayered composites Laminate construction type 11 gave the best 

combination of properties. The smaller CTBN coated GnP is the most effective. 

Laminate construction type 10 (80% porous ~24 micron GnP paper) showed the 

best impact energy without loss of mechanical properties.

In order to enhance the impact resistance between the GnP and vinyl ester, a 

greater degree of interaction between the xGnP and the resin is required through a 

larger number of functional groups and/or “strengthening” the xGnP. 

Incorporation of a Nylon 6 toughening polymer, which is compatible with the 

vinyl ester, shows better impact energy.

6.5.2 High-Speed Blast Testing

The “best” composite laminate was evaluated for blast damage resistance in 

collaboration with Professor Liu. The blast wave attained a speed of 

approximately 1000 m/s, and the sample size was 125 × 125 × 3.2 mm thick. The 

samples tested were the a) glass fabric /vinyl ester composite, type-4 laminate 

construction; b) CTBN-coated GnP-15, type-11 laminate construction; and c) 

40-μm GnP paper with 10% holes of 6-mm diameter in type-14 laminate 

construction (Fig. 6.8). The unmodified composite a) showed evidence of severe 

delamination, fiber and matrix cracking, and front surface damage due to the 

interaction of the high-temperature blast front with the front surface to the 

composite laminate. The incorporation of CTBN-modified GnP particles between 

lamina b) reduced damage and out-of-plane deformation under blast loading 

compared to the standard composition a). However, the incorporation of GnP 

“holey” paper between lamina c) reduced damage and out-of-plane deformation 

under blast loading. The holes produced controlled delamination perpendicular to 

the propagating blast front truncating through-the-thickness damage. The high 

thermal conductivity of the GnP paper in this composite appears to improve the 

damage reduction from interacting with the hot gasses at the blast front. 
84

a b

Plane view of Front

c

Fig. 6.8 Three composite samples (a, b, and c) after blast test

6.6 Future Directions

The insertion of GnP thin films fabricated with at least a 10% concentration of 

6-mm holes can significantly reduce blast damage to a composite through 

controlled lateral delamination and high lateral heat conduction. Since numerous 

combinations and permutations of hole size, placement, and concentration exist 

for each laminate construction, a combined experimental and modelling program 

would identify the optimum material combinations to make blast-resistant 

composites for Army military vehicles.

6.7 Acknowledgments

The graphene nanoplatelets used in this research was supplied by XG Sciences, an 

exclusive licensee of the MSU process for producing GnP. XG Sciences has a 

production facility operating at 80 tons per year capacity and sells GnP at a bulk 

price of approximately $15/lb depending on quantity and grade. 
85

7. Thrust Area 3C: Vehicle Survivability and Occupant Safety

Dahsin Liu, Guojing Li, and Dan Schleh

7.1 Scaling Effects in Composites Subjected to Blast Loading

7.1.1 Introduction

The motivation behind this study is to identify the feasibility of predicting the 

behavior of prototype composite structures in the real-world operations based on 

the results obtained from laboratory coupon tests. If a relation or even a 

correlation exists between the prototype and the coupon counterparts, it can

significantly ease the effort in composite structure design. However, issues 

concerning size scaling and loading complexity between coupon specimens and 

prototype structures must be carefully addressed. This study focuses on 

characterizing the scaling effect, if there is any, on the behavior of composites 

under high-pressure loading. No geometrical parameter in microscopic scale is 

considered. Only simple geometry, such as a plate, is of interest in the 

investigation.

7.1.2 Experimental Methods

Based on Buckingham’s theory, a linear scaling was required for specimen 

dimensions. Having considered the availability of composite material and the 

capability of testing facility, we determined a dimensional scaling ratio of 3:5:10 

for the investigation. Since the composite plates were made of plain-weave glass 

fabrics and an epoxy matrix, their thicknesses should be scaled similarly to that of 

the dimensional scaling ratio of 3:5:10. Accordingly, composite plates consisting 

of 3, 5, and 10 layers of fabrics were determined for the scaling investigation. The 

in-plane dimensions of the specimens were also chosen to be 75, 125, and 250 

mm, following the scaling ratio of 3:5:10. 

The specimen dimensions and thicknesses are summarized in Table 7.1.1. The 

true scaling ratio for the thicknesses from manufactured composites is not exactly 

the same as that based on the layer number. Besides, there was no scaling in the 

fiber microstructure, including fiber diameter, tow size, and cell size, since all 

composite materials were made of identical glass fabrics.
86

Table 7.1.1 Scaling parameters

Scaling Factor (3) (5) (10)

Specimen Diameter (mm) 75 125 250

Specimen Thickness (layer number / mm)

Scaling Factor 

3 / 2.01

(3)

5 / 3.12

(4.66)

10 / 6.07

(9.06)

Blast Tube Diameter (mm) 3.75 6.25 12.5

Blast Duration (ms)

Scaling Factor

6.3

(3)

11.7

(5.57)

23.3

(11.10)

7.1.3 Materials

A glass woven fabric and an epoxy matrix were chosen for this study because of 

their flexibility in making composite materials with various thicknesses and 

dimensions for scaling study. The glass fabric was of orthogonal plain weave, and 

the widths of both warp and fill tows were 9.5 mm (0.375 inches). There was no 

microscopic scaling because of the use of the fabric, i.e., the tow size for various 

specimen sizes was constant. In manufacturing composite specimens, an epoxy 

matrix was chosen for vacuum-assisted resin transfer molding processing based 

on its low viscosity and adequate properties. 

7.1.4 Experimental Setup

For simulating blast loading, a facility named the Laboratory Blast Simulator 

(LBS) was designed, constructed, and used. As shown in Fig. 7.1.1, the LBS is 

based on a piston-assisted shock tube because of its capability to produce pressure 

waves with a high pressure of 210 MPa, a high temperature around 1,000 °C, and 

a high speed about 5 Mach. To achieve a blunt wave front, i.e., a shock wave, as 

those that occurred in real blasts, a second diaphragm was installed right before 

the blast tube to convert the 80-mm-diameter pressure waves generated in the 

shock tube into smaller pressure waves, such as 3.75-, 6.25-, and 12.5-mmdiameter ones used for the scaling studies.

Fig. 7.1.1 Schematic (top) and photo (bottom) of the Laboratory Blast Simulator (LBS)
87

7.1.5 Testing and Results

Figure 7.1.2a shows the strain histories (based on the electrical resistance strain 

rosette attached close to the center of the 10-layer specimens), while Fig. 7.1.2b 

shows the deformation histories, based on projection moiré (see Fig. 7.1.2c), at 

the center of 10-layer specimens. The deformation results seem to be more 

consistent among different tests than the stain results. This may be because the 

strain gage is of a point technique and the associated results can be strongly 

affected by the local fiber structure, while the projection moiré is of a global 

technique and the associated results are less sensitive to the local material 

fluctuation. Both results seem to also reveal strong vibration and wave 

propagation during the high-pressure loading.

0

0.005

0.01

0.015

0.02

0.025

0 5 10 15 20

Time (ms)

Strain

10"-specimen #2-45 degree

10"-specimen #2-0 degree

0

2

4

6

8

10

12

14

16

0 1 2 3 4 5

Time(ms)

Deformation(mm)

10"-#1

10"-#2

 a) b) c)

Fig. 7.1.2 Comparison of a) strain histories, b) displacement histories, and c), a) and b)

Fig. 7.1.2 presents strain and deformation histories on the same diagram for 

comparison for a 10-layer specimen. Artificial justifications were made for 

qualitative comparison. It is interesting to see a similar trend between the strain 

history and the deformation history for the 10-layer specimen. The results seems 

to also indicate the strong effect of the wave front. A shock wave front in the 

10-layer specimen (250 mm in diameter) produces a steep strain increase while a 

gradually increased pressure wave front yields a mild strain increase in the 

beginning of strain history. The strain rates for all 3 cases can also be found from 

the strain histories. They are 230/s, 210/s, and 180/s for 3-, 5-, and 10-layer 

specimens, respectively. They are of medium strain ranges—even the pressure 

wave is as high as 105 MPa.

7.1.6 Conclusions

The high-pressure testing presented in this study is a unique technique for 

characterizing the stiffness of composite materials—so-called pressurizing 

stiffness. Three scaling cases in a ratio of 3:5:10 have been investigated. The 

strain rates of the cases studied were around 200/s. Considering experimental 

variations, we find that the experimental results show clear similarity among the 
88

deformation histories of the 3 cases after they are scaled by the respective 

dimensional ratios, implying that there is no significant effect due to scaling 

among the 3 sizes studied. The stiffness scaling ratio of the 3 scaling cases is 

3.00:5.14:9.22, similar to that of dimensional scaling ratio, further confirming the 

similarity among the 3 scaling cases.

7.1.7 Outcomes

1. Laboratory Blast Simulator (LBS) shown in Fig. 7.1.1 was developed.

2. Liu, Guojing and Liu, D., “Scaling Effect in Composite Plates Subjected to 

High-Pressure Waves,” Proceedings of the American Society for Composites, 

Twenty-Sixth Technical Conference, Montreal, Quebec, Canada, September 

26-28, 2011.
89

7.2 Developing Triaxial Quasi-3-dimensional (TQ3D) Woven 

Composites with High-Impact Resistance

Dahsin Liu, Corey Anderson, and Kirit Rosario

7.2.1 Introduction

Owing to their high stiffness, high strength, high energy absorption, and low 

density, fiber-reinforced polymer-matrix composites have been considered to be 

excellent lightweight materials with high impact resistance. With flat fibers, 

laminated composites have excellent in-plane properties for structures subjected 

to membrane stresses. However, they are prone to out-of-plane loading, which can 

cause severe delamination. To improve delamination resistance, 2-dimensional 

(2-D) orthogonal woven fabrics have been used because the identical property 

between adjacent layers gives low delamination cause. Three-dimensional (3-D) 

braided composites should also have high delamination resistance owing to the 

fibers along the thickness. However, their in-plane properties may be 

compromised by these fibers.

7.2.2 Development of Q3D Fabrics

In designing a composite with coordinated high in-plane strength and high 

impact-induced delamination resistance, we have developed a quasi-3-

dimensional (Q3D) woven composite because of its high degree of fiber 

straightness and minimum undulation (interlocking) between adjacent layers. A 

Q3D woven composite is not a 3-D composite since it does not have fiber yarns 

specifically oriented in the thickness direction. A Q3D woven composite, 

however, is a kind of 3-D composite because all the adjacent yarns interlocked 

with one another layer-by-layer to form a 3-D network. Fig. 7.2.1 (left) shows a 

through-thickness view of an interlocking network of a biaxial Q3D (BQ3D) 

woven composite. The degree of interlocking is minimal because each yarn only 

interlocks with the adjacent ones above and below it, hence retaining high inplane properties due to a low degree of undulation. Because the interlocks are 

huddles to delamination propagation, they provide efficient delamination 

resistance to the Q3D composites.
90

 

Fig. 7.2.1 Side view of BQ3D woven composite in a) warp and b) fill directions (left), top 

view of TQ3D woven composite (center), and TQ3D braiding machine (right)

7.2.3 Manufacture of TQ3D Fabric

A triaxial Q3D (TQ3D) woven composite has 3 yarns with 60o between any 2 

yarns. Fig. 7.2.1 (center) shows the top view of the TQ3D woven fabric. It looks 

like a 2-D triaxial woven composite. However, all the adjacent yarns in the 

thickness direction are interlocked with one another layer-by-layer through the 

thickness similar to the biaxial Q3D counterpart; hence, there is no interfacial 

layer with a pure matrix as those in laminated composites. A braiding machine, 

shown in Fig. 7.2.1 (right), has proved the manufacturability of the TQ3D fabric.

7.2.4 Testing Results

TQ3D woven composite has been proved to be superior to its laminated and 2-D 

woven counterparts. When a transverse force acts on it, the force can be 

efficiently distributed to all yarns in the plane and through the thickness via the 

tight interlocking system of the TQ3D weave, hence reducing potential local 

damage. Central impact results, given in Table 7.2.1, show that TQ3D composite 

[0/60/-60]4 has 12.5% and 20% higher stiffness than its 2-D woven [0/60/-60]4 

and laminated [0/60/-60]4 counterparts, respectively. The outstanding Q3D 

woven composites should be excellent candidate materials for high-performance 

combat vehicles exposed to constant dynamic impacts.

Table 7.2.1 Central impact induced bending stiffness

Laminated 2-D Woven Q3D

Average Bending Stiffness

(kN/mm) 0.456 0.485 0.546
91

7.2.5 Outcomes

1. The Braiding Machine for Preparing TQ3D Fabrics, Fig. 7.2.1b.

2. Liu, Dahsin, C.D. Anderson, K. Rosario and C.-F. Yen, “Development of 

Quasi-three-dimensional Woven Composites for Higher Impact Resistance,” 

SAMPE 2012, Baltimore, MD, May 21-24, 2012.
92

7.3 Developing Projection Moiré Assisted Impact Testing

PIs: Dahsin Liu, Brandon Gulker, Ryan Lureau, and Dan Schleh

7.3.1 Introduction

Measuring dynamic contact forces, such as those due to ballistic impact and crash 

load, is critically important to the investigation of the material and structure 

involved. Until an instrumented projectile capable of measuring direct contact 

force due to high-velocity impact is available (it is recently developed by the 

author), an indirect measurement method may be sought. This study uses an 

optical method, so-called projection moiré or fringe projection, to measure the 

out-of-plane deformation of the nonimpacted surface of a structure component 

under low-velocity impact to recover the contact force history on the contact 

surface.

7.3.2 Experimental Setup and Results

As a method for measuring full-field out-of-plane displacement, projection moiré 

provides high measuring quality with a simple experimental setup, as shown in 

Fig. 7.3.1. Based on an image-processing program developed by Heredia and 

Patterson, this study presents the implementation of projection moiré in 

instrumented low-velocity impact testing. The moiré fringe patterns are shown in 

Fig. 7.3.2a, and the out-of-plane displacement profiles are shown in Fig. 7.3.2b. 

Fig. 7.3.1 Schematic of the overall view (left) and the detailed view (right)
93

 a) b)

Fig. 7.3.2 a) Deformation contours from projection moiré for evenly spaced time step of 

0.33 mm and b) profile lines for loading

7.3.3 Validation and Applications

Fig. 7.3.3a shows that the results from projection moiré agree reasonably well 

with those obtained directly from the load cell. In an attempt to better document 

the composite response to impact loading, the possibility of correlating the 

external out-of-plane displacement measurement with the internal delamination is 

also investigated. It is found that the recovered out-of-plane displacement is able 

to show the propagation of the internal delamination (the bumps in the central 

portion of the curves in Fig. 7.3.2b) of the composites under impact loading. 

Besides low-velocity impact, the projection moiré technique can also be extended 

for applications involving ballistic impact. Figure 7.3.3b shows a setup for such 

application.

 a) b)

Fig. 7.3.3 a) Comparison between projection moiré (points) and load cell (continuous curve) 

and b) setup for ballistic impact
94

7.3.4 Outcomes

1. The Projection Moiré Assisted Ballistic Impact Testing System, Fig. 7.3.3b. 

2. Schleh, D. and Liu, D., “Fringe Projection in Horizontal Impact,” ICCES

2013, Bellevue, Washington, May 20-27, 2013.

3. Gulker, B.G., Lureau, R. and Liu, D., “Investigation of Impact Response of 

Composites with Projection Moiré Enhancement,” Experimental Mechanics, 

54(1), 35-43, 2014.
95

8. Thrust Area 6: Biomimetics

Srinivasan Arjun Tekalur

8.1 Enhanced Damage Tolerance in Biomimetic Ceramic 

Composites

8.1.1 Summary

The main objective of this thrust area was to use nature as an inspiration to design 

a new class of impact-resistant materials. There exists a great demand to generate 

lightweight, durable, and energy-efficient structural materials. In order to attain 

this goal, researchers have been studying design guidelines that exist in natural 

composites in order to implement the same in the development of synthetic 

composites. Of the innumerable structural biological composites found in nature, 

the nacreous layer in seashells has received significant attention owing to its 

unique architecture and exceptional mechanical properties. From the perspective 

of structural application, it is imperative to understand the suitability/applicability 

of these types of composites, should they be subjected to a variety of loading 

rates. In our investigations, we have attempted to address why bioinspired nacremimetic composites are well suited for impact loading conditions. We have also 

tried to understand the mechanics of these types of composites when subjected to 

impact loading and unearth the parametric attributes that are responsible for 

superior resistance. Additionally, interpretation is provided to understand what it 

takes for the nacreous layer to promote significant fracture resistance and weight 

reduction. Excerpts from our investigations provide beneficial guidelines in 

designing tough bioinspired composites at any length scale.

8.1.2 Introduction

Over the past decade, research has been aimed at producing composites that are 

both lightweight and durable via extracting design principles from structural 

biological composites (e.g., nacre). The inner shining layer in seashells is referred 

to as the nacreous layer and exhibits a staggered architecture in the form of an 

overlapping arrangement of mineral tablets in a soft polymeric matrix (Jackson et 

al. 1988; Sarikaya et al. 1990). What makes nacre an ideal model for biomimetic 

inspiration are 2 promising aspects: light weight and superior toughness (Barthelat 

and Espinosa 2007; Jackson et al. 1988; Kessler et al. 1996; Kamat et al. 2000;

Sarikaya et al. 1990). Light weight is accomplished via embedding a polymeric 

matrix in a network of ceramic/mineral bricks, whereas toughness has been 

attributed to existence of a multitude of toughening mechanisms—namely, tablet 
96

pull-out (Jackson et al. 1988), crack deflection through the biopolymer (Menig et 

al. 2000), platelet interlocks (Katti et al. 2005), presence of nanoasperities (Evans 

et al. 2000; Wang et al. 2001), diffusive tablet sliding (Barthelat et al. 2007) 

arising from tablet waviness, aragonite bridge reinforcements at interface (Song et 

al. 2003), relocking of tablets (Meyers et al. 2008) due to persistent contact of 

broken aragonite bridges, and synchronized deformation twinning of the 

nanoscale particles in ceramic bricks (Huang et al. 2011). All of the 

aforementioned factors contribute to both interfacial strengthening and fracture 

resistance in varying degrees of resistance. However, there is a lack in 

establishing the nexus between characteristic overlap length and optimized joint 

performance; and, thus, in order to address this aspect, we chose the nacreous 

layer in seashells as our model material for the purpose of investigation and in 

presenting the corresponding design guideline. 

Existing literature studies associate global toughness of nacre on the basis of 

numerous mechanisms, including crack deflection by the biopolymer. In our 

investigation we have approached the problem from a local perspective by 

considering a unit cell. Multifarious factors are associated with toughening in 

nacre. In our current article, we hypothesize that crack deflection by the 

polymeric matrix is the most dominant mechanism associated with nacre. We 

have found that the toughness contribution associated with crack deflection is 

raised significantly via the choice of optimal overlap length in the unit cell. This, 

in turn, reduces the strain energy release rate ahead of the crack tip (locally) to the 

lowest possible extent apart from promoting tortuosity in subsequent unit cells. 

Using fracture mechanics concepts, we observed that the choice of characteristic 

overlap length is based upon minimization of strain energy release rate (SERR) 

ahead of crack tip. To illustrate this, we have developed an analytical model to 

calculate crack-tip fracture toughness. Note that in our current investigation, we 

are not trying to predict the global fracture toughness of nacre/nacre-mimetic 

composites. Additionally, the micromechanics associated with crack deflection 

and jump from one unit cell to another resulting in a highly tortuous crack path is 

also explained in the current article. From the context of biomimetics, valuable 

insights can be drawn with respect to microstructure-property relationship. 

Excerpts drawn from the current investigation would provide beneficial 

guidelines in the choice of characteristic/optimal overlap lengths, under quasistatic loading, while designing adhesively bonded structures.
97

8.1.3 Analytical Model

We develop our analytical model by taking into consideration a unit cell with 

plane strain infinitesimal deformations, as shown in Figs. 8.1b and c. The

following assumptions are associated with the analytical model:

• 1-dimensional analysis based on “shear-lag” theory (Volkersen 1938).

• Participating members exhibit linear elastic behavior (ceramic bricks, 

biopolymer).

• Normal stress distribution in the ceramic bricks (no bending).

• Shear stress distribution in the biopolymer.

• Since the biopolymer is very thin compared to the brick thickness, the 

stress distribution is considered constant throughout the joint thickness 

(Luo and Tong 2004).

• There is no rotation of the transverse normal about the y-axis, which is 

directed into the plane of the paper. 

The unit cell has been discretized into 4 elemental blocks (labeled 1 to 4). The 

total width of the mineral tablets is 2b, and the thickness of the biopolymer is 

given by η. Local rectangular Cartesian coordinate systems for elemental blocks 

1–4 have been defined as shown in Fig. 8.1b. It has also been assumed that there 

is no rotation of the transverse normal about the y-axis.
98

Fig. 8.1 a) Schematic of staggered architecture of calcium carbonate bricks (shown in 

green) in biopolymer matrix (shown in yellow); b) expanded view of the 2-dimensional (2-D) 

unit cell structure (with plane strain infinitesimal deformation) showing the precracked 

length (L1) and instantaneous overlap length (L2) and coordinate systems; c) expanded view 

of the 2-D unit cell structure (with plane strain infinitesimal deformation) highlighting the 

elemental blocks and the original overlap length (Lo); d) splitting of parent crack tip for 

SERR calculations; and e) schematic identifying shear and normal force resultants Nc and 

Qc, respectively

8.1.3.1 Computation of Strain Energy Release Rate 

Considering plane strain conditions, we assume a virtual crack tip displacement 

by an amount da . The parent crack tip at point C splits into points A and B, and 

translates to point C'. From Dugdale’s cohesive strip model, SERR ahead of a 

crack-tip due to incremental increase in crack length is equivalent to work (or, 

rather, energy) required to close that infinitesimal increment and is thus computed 

as follows (Chadegani 2008):

( ) ( ) 1

2

BA BA

cx x cz z

W G Nu u Qu u da da

== −+ −    , (8.1)

where W represents the work required to close the virtual crack. As shown in 

Figs. 8.1d and e, shear and normal force resultants, Nc andQc , respectively, can 

be computed as follows:

0

da

p

c xz

x

N dx σ

=

= − ∫ . (8.2)
99

0

da

p

c zz

x

Q dx σ

=

= ∫ . (8.3)

p σ xz and p σ zz are obtained from the solution of the problem prior to virtual crack-tip 

extension. A

x u , Az u , Bx u , and B

z u are found out by solving the problem by 

substitution of overlap length L La 2 0 = −o by L L a da 2 0 = −− o and by using the 

following relations:

2 0

A 2

x x x a da

u u = − = . (8.4)

2 0

A 2

z z x a da

u u = − = . (8.5)

1 0

B 1

x x x a da

u u = − = . (8.6)

1 0

B 1

z z x a da

u u = − = . (8.7)

In order to investigate the effect of the SERR component that contributes to crack 

propagation, Eq. 8.8 can alternatively be written as 

( ) 1

2

B A G Nu u II c x x da =   −  . (8.8)

( ) 1

2

B A G Qu u I cz z da =   −  . (8.9)

1 tan II

I

G MMP

G

−   =    

. (8.10)

8.1.4 Results and Discussion

To test the validity of the analytical model, the variation of shear stress at the 

mineral biopolymer interface is plotted for varying overlap lengths and pre-crack 

in the unit cell, as shown in Fig. 8.2. The properties of the mineral component in 

the nacreous layer (Barthelat et al. 2006) are E1 = 144 GPa, E2 = 76 GPa, E3 = 

82 GPa, G12 = 47.2 GPa, G13 = 25.6 GPa, G23 = 41.3 GPa, ν12 = 0.44, ν13 = 

–0.06, ν23 = 0.18, 2b = 0.5 μm. The properties of the biopolymer layer in nacre 

(Xu et al. 2011) are Ep = 10.57 GPa, Ga = 1.4 GPa, νa = 0.45, η = 20 nm. The 

observed overlap length in the nacreous layer is reported to be 1.67 μm (Espinosa 

et al. 2011).
100

Fig. 8.2 Variation of interfacial shear stress (at the extremities of the overlap) as a function 

of varying overlap length and precrack. The figure in the inset shows the expanded view for 

lower shear levels in the unit cell.

When an external load is applied, interfacial shear stress attains a maximum value 

at the extremities of its overlap. As it can be seen from Fig. 8.2, the maximum 

interfacial shear stress (at the ends of the joint) attains a minimum value for 

overlap length exceeding 2–2.5 μm and reaches the shear strength of the 

biopolymer ≡ 37 MPa (Jackson et al. 1988). This observation is in direct 

agreement with those obtained by Dutta et al. (2013) in their estimation of 

characteristic overlap length in nacre under dynamic rates of loading, 

respectively. The highlighted light-blue region on the right-hand side of 2–2.5 μm 

might be considered the safe zone, as it contributes to a minimization of shear 

stress at the interface and optimizes inter-mineral load transfer via shear 

deformation of the intermediate biopolymer.

However, we hypothesize that under quasi-static rates of loading, an interplay 

exists between crack-tip fracture toughness and overlap length that not only leads 

to a state of minimization of SERR ahead of the crack tip but also influences 

crack tortuosity in the nacreous layer. Figure 8.3 shows the variation in SERR 

ahead of the parent crack tip with increasing crack length for reported overlap 

length in the nacreous layer. Fracture toughness of the organic matrix varies 

between 30 and 160 mJ/m2 (Song et al. 2003), whereas intrinsic fracture 

toughness of the organic interface has an upper limit varying between 400 and 
101

1,000 mJ/m2

. This value is comparable to the mineral toughness in the nacreous 

layer. This wide variation can be associated with the viscoelastic nature of the 

biopolymer, the presence of nanoasperities, mineral bridges, or a combination of 

all the modes. Once the strain energy release rate ahead of the crack tip reaches its 

critical value, the crack will start to grow. As the crack size increases, the void 

within the unit cell also increases; therefore, the x-axis in Fig. 8.3 corresponds to 

(increasing) crack volume in the unit cell.

Fig. 8.3 SERR ahead of the crack tip with increasing crack in the unit cell

As mentioned previously, given the nature of biological composites, there are 

multiple critical toughness values reported that arise from one or a combination of 

multiple parameters (for instance, toughness associated with the viscoelastic 

nature of the biopolymer, nanoasperities, mineral bridges or a combination of a 

few or all of those). Figure 8.3 attempts to address how these barriers are passed 

over once the crack progresses and the strain energy release rate ahead of the 

crack tip starts increasing with progressive crack growth. de Gennes and Okumura 

(2000) and Okumura and de Gennes (2001) presented an analytical solution for a 

layered system based on the laminar architecture of the seashells. They also 

included the effects of a weaker stress concentration ahead of the crack tip in 

these materials in comparison to traditional isotropic elastic materials under quasi-
102

static loading conditions. However, the role played by the existence of 

characteristic overlap length on weakening the SERR associated with crack-tip 

advance in the nacre/nacre-mimetic materials has not been investigated yet. 

8.1.5 Inferences

In our current investigation, an analytical model has been developed to explain 

the reason for the highly tortuous crack path in the nacreous layer in seashells. It 

has also been observed that characteristic overlap length exists in the nacreous 

layer, which contributes to the minimization of the strain energy release rate 

ahead of the crack tip and thereby allays fracture and raises fracture resistance 

against crack growth. Using the analytical model, we have investigated the crack 

growth conditions in detail, which explains the reason associated with crack 

jump/deflection at multiple interfaces. Excerpts drawn from the current study 

would provide beneficial guidelines in designing lightweight and tough 

adhesively bonded structures. In addition to what is shown above, our thrust area 

research has shown experimentally that these biomimetic composites are indeed 

superior in terms of mechanical properties and fracture resistance while compared 

with neat ceramic materials. Additionally, they offer weight savings of up to 45% 

when compared with using neat ceramic alone.

8.1.6 Outcomes

1) Abhishek Dutta, Srinivasan Arjun Tekalur, “Crack tortuosity in the nacreous 

layer – Topological dependence and biomimetic design guideline,”

International Journal of Solids and Structures, v 51, January 2014, p325–335 

2) Abhishek Dutta and Srinivasan Arjun Tekalur, “Synthetic staggered 

architecture composites”, Materials and Design, v46, April 2013, p 802–808 

3) Abhishek Dutta, Srinivasan Arjun Tekalur and Milan Miklavcic, “Optimal 

overlap length in staggered architecture composites under dynamic loading 

conditions”, Journal of Mechanics and Physics of Solids, v61, n1, January 

2013, p145-160

4) Abhishek Dutta, Simona Fermani, Srinivasan Arjun Tekalur, Abigail 

Vanderberg and Giuseppe Falini, “Calcium phosphate scaffold from biogenic 

calcium carbonate by fast ambient condition reactions”, Journal of Crystal 

Growth, v336, n 1, December 2011, p50–55
103

8.1.7 References

Barthelat, F., Li, C.-M., Comi, C., Espinosa, H.D. 2006. “Mechanical properties 

of nacre constituents and their impact on mechanical performance,” J. Mater. 

Res. 21(8), 1977–1986.

Barthelat, F., Espinosa, H.D., 2007. “An Experimental Investigation of 

Deformation and Fracture of Nacre-Mother of Pearl,” Exp. Mech. 47, 311–

324.

Barthelat, F., Tang, H., Zavattieri, P., Li, C.M., Espinosa, H., 2007. “On the 

Mechanics of Mother-of-Pearl: A Key Feature in the Material Hierarchical 

Structure,” J. Mech. Phys. Solids 55, 306–337.

Chadegani, A., 2008. “Strain energy release rate of adhesive-bonded composite 

joints with a prescribed interlaminar crack,” M.S. Thesis-Wichita State 

University (http://hdl.handle.net/10057/2023).

de Gennes, P.G., Okumura, K., 2000. “On the toughness of biocomposites,” C. R. 

Acad. Sci. Paris, t.1, Ser. IV, 257–261.

Dutta, A., Tekalur, S.A., Miklavcic, M., 2013. “Optimal length scales in staggered 

architecture composites under dynamic loading conditions,” J. Mech. Phys. 

Solids 61(1), 145–160.

Espinosa, H.D., Juster, A.L., Latourte, F.J., Loh, O.Y., Gregoire, D., Zavattieri, 

P.D., 2011. “Tablet-Level Origin of Toughening in Abalone Shells and 

Translation to Synthetic Composite Materials,” Nature Comm. 2, 173.

Evans, A., Suo, Z., Wang, R., Aksay, I., He, M., Hutchinson, J., 2001. “Model for 

the Robust Mechanical Behavior of Nacre,” J. Mater. Res. 16, 2475–2484.

Huang, Z., Li, H., Pan, Z., Wei, Q., Chao, Y.J., Li, X., 2011. “Uncovering highstrain rate protection mechanism in nacre,” Sci. Rep. 1, 148; 

DOI:10.1038/srep00148.

Jackson, A.P., Vincent, J.F.V., Turner, R.M., 1988. The mechanical design of 

nacre. P. R. Soc. London B. 234, 415–440.

Kamat, S., Su, X., Ballarini, R., Heuer, A.H., 2000. “Structural basis for the 

fracture toughness of the shell of the conch Strombus gigas,” Nature, 405, 

1036–1040.

Katti, K.S., Katti, D.R., Pradhan, S.M., Bhosle, A., 2005. “Platelet interlocks are 

the key to toughness and strength in nacre,” J. Mater. Res. 20, 1097–1110.
104

Kessler, H., Ballarini, R., Mullen, R.L., Kuhn, L.T., Heuer, A.H., 1996. “A 

biomimetic example of brittle toughening: (I) steady state multiple cracking,” 

Comp Mater Sci. 5, 157–166.

Luo, Q., Tong, L., 2004. “Linear and higher order displacement theories for 

adhesively bonded lap joints,” Int. J. Solids Struct. 41(22–23), 6351–6381.

Menig, R., Meyers, M.H., Meyers, M.A., Vecchio, K.S., 2000. “Quasi-static and 

dynamic mechanical response of Haliotis rufescens (abalone) shells,” Acta 

Mater. 48, 2383–2398.

Meyers, M., Lin, A., Chen, P., Muyco, J., 2008. “Mechanical Strength of Abalone 

Nacre: Role of the Soft Organic Layer,” J. Mech. Behav. Biomed. Mater. 1, 

76–85.

Sarikaya, M., Gunnison, K.E., Yasrebi, M., Aksay, J.A., 1990. “Mechanical 

propertymicrostructural relationships in abalone shell,” Mater. Res. Soc. 174, 

109–116.

Song, F., Soh, A., Bai, Y., 2003. “Structural and mechanical properties of the 

organic matrix layers of nacre,” Biomaterials 24, 3623–3631.

Volkersen, O., 1938. Die Nietkraftverteilung in zugbeanspruchten 

Nietverbindungen mit konstanten Laschenquerschnitten, Luftfahrtforsch 15, 

41.

Wang, R., Suo, Z., Evans, A., Yao, N., Aksay, I., 2001. “Deformation 

mechanisms in nacre,” J. Mater. Res.16, 2485–2493.
105

9. Thrust Area 7: Design and Manufacturing

9.1 Experimental and Numerical Characterization of Flexural 

Behavior of Vacuum-Assisted Resin Transfer Molding 

(VARTM)-Infused Composite Sandwich Structures

R Umer, EM Waggy, M Haq, and AC Loos

9.1.1 Summary

Experimental and numerical characterization of sandwich materials is required to 

fully exploit the benefits offered by these materials and to efficiently design them. 

In this work, the flexural behavior of resin-infused composite sandwich structures 

was investigated. Panels with 2 different polyvinylchloride foam densities and 

thicknesses were studied. The S-2 Glass fabric face sheets were resin infused and 

bonded to the core in a single-step process using epoxy resin. Sandwich beams 

machined from the panels were subjected to 3- and 4-point bending tests. Finite

element–based simulations predicting the flexural response of the sandwich 

panels were performed and compared with experiments. Excellent agreement in 

finite element–predicted failure loads and experiments were observed.

9.1.2 Introduction

Composite sandwich structures have been used extensively for aerospace, marine, 

and ground vehicle applications because of their high specific strength and 

stiffness, high shock resistance, and resistance to corrosion. Polymeric foams 

derived from materials such as polyvinylchloride (PVC), polyurethane, 

polyethylene terephthalate, expanded polystyrene slabs, etc., are gaining attention 

as the core material for sandwich structures for a variety of applications [1–3]. A 

wide range of characterization and resulting properties of sandwich structures are 

possible, depending on the materials used, their configurations, and testing 

schemes. An experimentally validated, simulations-based approach can eliminate 

the costly trial and error experiments and can be used as a design tool for such 

complex materials and structures [4]. Such an approach is being implemented in 

this work with respect to the flexural behavior of sandwich structures.

Although extensive research has been devoted to the flexural behavior of 

composite laminates, the flexural behavior of sandwich structures is limited [5–8]. 

In this context, considerable work describing the static flexural behavior of 

sandwich beams has been reported [9–11]. It is commonly agreed that the failure 

of a sandwich structure can occur through several damage mechanisms [12–17],
106

such as 1) skin compressive/tensile failure, 2) core shear failure, 3) delamination, 

i.e., skin–core debonding, 4) local skin wrinkling, and 5) core indentation. One of 

the main issues limiting the accurate modeling of the sandwich structures is the 

intrinsic anisotropy and the non-homogeneity of the sandwich structures [18]. 

This issue can be overcome with detailed experimental characterization and 

accurate modeling. Additionally, it is reported that the presently available data on 

mechanical properties necessary to allow a correct and reliable design of complex 

sandwich structures is not sufficient [18]. One way to address this issue is to 

develop and implement experimentally validated simulations. A limited number 

of critical experiments are performed, and computational simulations that 

accurately and realistically model the behavior of the components are created. 

These simulations are validated by the experiments and then can be used as a 

design tool to extrapolate the experimental matrix. The use of this approach is 

attempted in this work, and the developed simulations are compared with actual 

experimental response.

Considerable work on theoretical and numerical analysis of sandwich panels 

exists and is well documented [18]. Also, the failure modes of sandwich beams 

under static [8,19,20] and cyclic loads [21] have been investigated. The numerical 

models that study the progressive collapse of the sandwich structures have also 

been reported [19–24]. The data reported in the literature show a large variation 

depending on the type of materials used, material and experimental 

configurations, parameters sought, and applications. Nevertheless, it is commonly 

agreed [18–24] that the accuracy of the simulations depends on the realistic 

modeling and accurate properties of the constituents. Hence, the simulations 

performed in this work use experimentally characterized material properties and 

are compared with experimental flexural response of the various experimental 

tests. 

In this work, sandwich panels created with foams having 2 different densities and 

thicknesses were studied. Additionally, the thickness of face sheets (skins) was 

varied depending on the core thicknesses and the test setup as per ASTM C-393 

requirements. Finite element–based simulations using experimentally 

characterized material properties were used to study the flexural behavior of the 

vacuum-assisted resin transfer molding (VARTM)-infused composite sandwich 

panels. The details on materials, VARTM infusion, sample preparation, testing 

schemes, simulations, and results are provided in the following sections. Overall, 

experimentally validated simulations can be used to predict the performance of 

novel sandwich panels beyond the existing experimental data and hence can be 

used as an efficient design tool.
107

9.1.3 Experiments

9.1.3.1 Materials

The reinforcement used for the face sheets was Owens Corning ShieldStrandS, 

S-2 Glass plain weave fabric with an areal weight of 818 g/m2. The core material 

consisted of DIAB Divinycell [25] nonporous H130 and H250 PVC foams with 

thicknesses 25.4 and 12.7 mm, respectively. The distribution medium was 

AIRTECH Advanced Materials Group Resinflow 60 low-density/high-density 

polyethylene blend fabric. The resin used was 2-part toughened epoxy—namely, 

SC-15 obtained from Applied Poleramic. 

9.1.3.2 Resin Infusion

The sandwich panels were fabricated using VARTM. A steel mold (609.6 × 914.4 

mm) with point injection and point vent was used to fabricate 304.8- × 609.6-mm 

sandwich panels. A schematic of the VARTM layup is shown in Fig. 9.1.1. The

number of layers of glass fabric in each face sheet depends on the thickness of the 

foam core. For 25.4-mm-thick foam panels, 4 plies of glass fabric were used on 

both of the face sheets, but for the 12.7-mm-thick foam, only 2 plies on each side 

were used, according to design criteria specified in ASTM C-393 [26]. Two layers 

of resin distribution medium were used over the top face sheet, and only one layer 

of distribution was used under the bottom face sheet. This was done to equalize 

the resin infiltration times for the top and bottom face sheets, resulting in uniform 

flow and reproducible manufacturing of the panels. After the materials were placed, 

the mold was sealed using a vacuum bag and sealant tape. The mold was then 

infused under vacuum with a pressure of 1 atm. The resin-infused sandwich panel 

was cured in a convection oven at 60 °C for 2 h and postcured at 94 °C for 4 h.

Fig. 9.1.1 Layup for the VARTM process
108

9.1.3.3 Experimental Test Setup and Procedure

The 3-point flexural test specimens were prepared in accordance with ASTM 

C-393 standards [26]. The 3-point flexural test uses a 150-mm support span with 

the center loading. The specimen length and width was 200 and 75 mm, 

respectively. The 4-point flexural test specimens were prepared in accordance 

with ASTM D-7249 standards [27]. The 4-point flexural test specimens have a 

560-mm support span length, with a loading span length of 100 mm. The 

specimen length and width were 610 and 75 mm, respectively. Both 3- and 

4-point flexural tests were conducted on panels containing both H130 (25.4-mm 

thickness) and H250 (12.7-mm thickness) foams. A schematic of 3- and 4-point 

flexural tests is illustrated in Fig. 9.1.2.

 S / 2 S / 2

S

P1

a)

 S / 2 S / 2

S

P/2 P/2

L

b)

Fig. 9.1.2 a) 3-point loading ASTM C393 standard configuration, S = 150 mm and b) 

4-point loading ASTM D7249 long beam standard configuration, S = 560 mm, L = 100 mm

The flexural testing was performed on an MTS Insight 100 material testing 

workstation. Figure 9.1.3 shows the experimental setup for both 3- and 4-point 

tests. All the supports and loading bars used cylindrical pivots, as shown in Fig. 

9.1.4. The test setup consisted of a base comprising a steel beam with a T-slot that 

allowed movement of support blocks to vary the span. The support block holds 

12.7-mm-diameter steel bars (cylindrical pivot) that allow free rotation of the 

specimen at the loading and support points. Rubber pressure pads were used on 

the flat steel loading block to prevent any local failures at the face sheets. The 
109

rubber pads having a Shore-A durometer of 55 and thickness of 3 mm were glued 

to the loading block to prevent slipping. The loading and support bars were 

75 mm long, equaling the width of the test specimens. A laser extensometer was 

used to measure the midspan deflection of beams.

a)

b)

Fig. 9.1.3 Setup of a) 3-point and b) 4-point bending fixture
110

Fig. 9.1.4 Cylindrical pivot loading and support bar

The test specimens were carefully placed on the support bars to ensure that the 

loading was symmetric and that the sample was level. The test was performed at a 

loading rate of 6 mm/min, and data were recorded at a sampling rate of 2.5 Hz. 

An average preload force of (100 N) was applied initially to ensure that the 

loading bar was also level and symmetric with the test specimen. The specimens 

were tested until failure. The flexural stiffness was then calculated as per 

procedure described in ASTM standard D 7250 [28]. 

9.1.4 Numerical Modeling

Numerical simulations were performed to evaluate the flexural behavior of 

sandwich panels tested in this study. In this work, numerical simulations were 

conducted using Micross [29], a finite element–based multiscale analysis tool for 

the modeling of bending and in-plane shear tests of sandwich panels. Micross is a 

part of the commercially available finite element package Digimat [30]. The 

software creates 3-dimensional models of the sandwich panels with inputs of 

material properties and dimensions of the facing and the core. The number of 

layers and orientations of the laminae in the skins were also provided as an input. 

Also, the loading and boundary conditions, including the width of the loading 

pads, were provided as input for both 3- and 4-point loading configurations. The 

facing sheets were modeled as a linear elastic orthotropic material. The tensile 

properties of the facing sheets were experimentally characterized, and the fracture 
111

properties were obtained from literature [31], corresponding to the materials 

similar to the ones used in this work. The properties of the core were also 

modeled as linear elastic material and were obtained from the manufacturer [25]. 

Tables 9.1.1 and 9.1.2 provide the summary of material properties used in the 

simulations.

Table 9.1.1 Foam core properties [25]

Foam Density

(kgm-3

)

Tensile 

Modulus

(MPa)

Tensile 

Strength

(MPa)

Shear 

Modulus

(MPa)

Shear 

Strength

(MPa)

Compressive 

Modulus

(MPa)

Compressive 

Strength

(MPa)

H130 130 175 4.8 50 2.2 170 3.0

H250 250 320 9.2 104 4.5 300 6.2

Table 9.1.2 Material properties of plain weave S-2 Glass/SC-15 epoxy laminates

Young’s Modulus (Exa, Eya, Ez) (GPa) 24.85, 24.85, 11.04

Shear Modulus (Gxy, Gyz, Gxz) GPa 2.67, 1.97, 1.97

Poisson’s Ratio (xy, yz, xz) 0.11, 0.18, 0.18

Axial Tensile Strengtha (MPa) 490

Axial Compressive Strength (MPa) 238

Transverse Tensile Strength (MPa) 58

Transverse Compressive Strength (MPa) 58

Shear Strength (MPa) 62 a Experimentally measured, remaining data obtained from Deka et al. [31]

Micross automatically generates the finite element mesh for the assembled 

sandwich panel corresponding to the loading and the boundary conditions. The 

mesh consists of hexahedral elements for the core and shell elements for the 

facing sheets. The core and facing elements share the same nodes at the interface. 

The interaction at the interface is simply tied and does not include sliding 

conditions or cohesive zones. The models had approximately 3,200 elements with 

an average element size of 5 mm. Also, the failure theories to predict skin failure 

based on maximum allowable stresses and global failure theories based on TsaiWu, Tsai-Hill, and Azzai-Tsai-Hill theories are built in the software to predict the 

failure loads, stress/strain states, and the locations of failure. In this work, the 

flexural response corresponding to the experimental tests were simulated. Hence, 

four cases were simulated: a) 3-point bending with 12.7-mm H250 foam, b) 

3-point bending with 25.4-mm H130 foam, c) 4-point bending with 12.7-mm 

H250 foam, and d) 4-point bending with 25.4-mm H130 foam. The peak loads at 

failure and the flexural response were compared with experiments.
112

9.1.5 Results and Discussion 

9.1.5.1 3-Point Bending Tests

Six specimens were tested in 3-point bending for each type of core. The only 

acceptable modes of failure were compressive facing failure due to bending on the 

top or tensile facing failure due to bending on the bottom. The applied load versus 

deflection data sets for successful tests corresponding to the panels with H250 

foam cores are plotted in Fig. 9.1.5 along with a photograph of a representative 

failed specimen. The load-deflection response shows a linear behavior before 

failure. Figure 9.1.6 shows the load-deflection responses for successful tests and a 

photograph of a representative failed specimen corresponding to panels with the 

H130 foam core. As expected, the H130 response was found to be different 

compared to the H250 foam core panel. The curve was linear until an average 

applied load of 6 kN and then tends to become nonlinear because of core 

crushing, as shown in Fig. 9.1.6. This is mainly attributed to low density of H130.
113

0

1

2

3

4

5

6

7

8

9

10

0 2 4 6 8 10 12

Deflection (mm)

Load (kN)

Test 1

Test 2

Test 3

Test 4

a)

b)

Fig. 9.1.5 a) 3-point bending experiment for H250, 12.7-mm foam core sandwich panel, and 

b) sample under load
114

0

1

2

3

4

5

6

7

8

9

10

0 2 4 6 8 10 12 14 16

Deflection (mm)

Load (kN)

Test 1

Test 2

Test 3

a)

b)

Fig. 9.1.6 a) 3-point bending experiment for H130, 25.4-mm foam core sandwich panel, and 

b) sample under load

9.1.5.2 4-Point Bending Tests

The 4-point flexural response for sandwich panels with the H250 foam core is 

presented in Fig. 9.1.7 along with a photograph of a representative failed 

specimen. The tests were found to be repeatable, and the average peak loads were 

found to be around 3 kN prior to failure for all specimens. All failures occurred in 

the top face sheet. The flexural responses for panels with H130 foam core are 

presented in Fig. 9.1.8 along with a photograph of a representative failed 

specimen. The recorded data were used to calculate the flexural stiffness (D) as 

per ASTM D-7250 recommendations using Eq. 9.1.1. A single 4-point loading 
115

configuration and another 3-point loading configuration are required to obtain the 

flexural stiffness (D):

3 2 2

1 1 2 1

1 11 2 2 2 1

3 (141 121 / )

1936 (11 9 / )

PS S S D

PS PS

− = ∆− ∆ ∆ , (9.1.1)

where P represents the applied load, S1 is the support span in the 4-point loading 

configuration, S2 is the support span in the 3-point loading configuration, and Δ1

and Δ2 are deflections under 3- and 4-point loading configurations, respectively, 

corresponding to the applied load. The flexural stiffness of the H250, 12.7-mmthick foam core specimens was found to be 7.54 N-m2 with a standard deviation 

of ±0.823 N-m2. The flexural stiffness of the H130, 25.4-mm-thick foam core 

specimens was found to be 46.5 N-m2 with a standard deviation of ±4.41 N-m2. 

The results are also illustrated in Fig. 9.1.9.

0

0.5

1

1.5

2

2.5

3

3.5

0 20 40 60 80 100 120

Deflection (mm)

Load (kN)

Test 1

Test 2

Test 3

Test 4

Test 5

a)

b)

Fig. 9.1.7 a) 4-point bending experiment for H250, 12.7-mm foam core sandwich panel, and 

b) sample under load
116

0

1

2

3

4

5

6

7

8

0 10 20 30 40 50 60 70 80

Deflection (mm)

Load (kN)

Test 1

Test 2

Test 3

a)

b)

Fig. 9.1.8 a) 4-point bending experiment for H130, 25.4-mm foam core sandwich panel, and 

b) sample under load
117

0

10

20

30

40

50

60

12.7 25.4

Foam Thickness (mm)

Flexural Stiffness (N - m2

)

Fig. 9.1.9 Flexural stiffness for H250, 12.7-mm, and H130, 25.4-mm foam sandwich panels

9.1.5.3 Simulation Results

The flexural behavior of sandwich panels corresponding to various experimental 

tests was simulated. For brevity, the comparison and discussion of predicted 

flexural response from the simulations and experiments are reported only for the 

4-point bending cases. A similar analogy applies for the 3-point cases. 

Nevertheless, for the sake of completion, the peak flexural loads from all 4 cases 

are reported. The software used to perform the numerical simulations (Micross) 

allows 3 mesh refinement levels: coarse (~1000 elements), standard (~3000 

elements), and fine (~9000 elements). An initial mesh-refinement study revealed 

that the results from standard and fine meshes agreed well both in terms of 

capturing the failure mode and the load-carrying capacity. All of the following 

simulations followed standard mesh refinement and were found to agree well with 

experiments.

Figure 9.1.10 provides the longitudinal strains and equivalent stresses for the 4-

point bending case with 12.7-mm H250 foam. The loading and boundary 

conditions are also shown in Fig. 9.1.10. Similar images for longitudinal 

strains/stresses were obtained for the other cases but are not shown here for 

brevity. Figure 9.1.11 compares the predicted flexural response with experiments 

for the sandwich beams subjected to 4-point bending. An excellent agreement 

between experiments and simulations was observed throughout the complete 

flexural response for sandwich panels with H250, 12.7-mm foam thickness. This 

agreement was not observed for the case of H130, 25.4-mm foam beams, wherein 

good agreement was found only in the linear regime. At applied loads near the 
118

ultimate, the simulation predictions deviate from the experimental response. This 

was expected, as the simulations were performed with linear elastic properties for 

both the core and the facing sheets, thereby resulting in a linear response. In 

reality, at loads near the ultimate, complex mechanisms such as local shear 

deformations in the foam and micro-cracking contribute to softening and increase 

in the total displacements. Similar results have been reported by Feldhusen et al. 

[32], thereby supporting the results in this study. Also, the core used in the 

25.4-mm H130 beams is less dense than the 12.7-mm H250 foam, causing higher 

nonlinear deformations in the core. Nevertheless, the simulation-predicted failure 

type (face sheet) and peak loads agreed well with the experiments.

a)

b)

Fig. 9.1.10 Flexural analysis of sandwich panel subjected to 4-point bending: a) longitudinal 

strains (ε11) and b) equivalent stresses (megapascals). (Images obtained as output from 

Digimat/Micross.)
119

0

1000

2000

3000

4000

5000

6000

7000

8000

0 10 20 30 40 50 60 70 80 90 100

Displacement at midspan, lower facing sheet (mm)

Total Applied load (N)

Experiment - 4 point - 12.7 mm

FE - 4 point - 12.7 mm

Experiment - 4 point - 25.4 mm

FE - 4 point - 25.4 mm

0

1000

2000

3000

4000

5000

6000

7000

8000

0 10 20 30 40 50 60 70 80 90 100

Displacement at midspan, lower facing sheet (mm)

Total Applied load (N)

Experiment - 4 point - 12.7 mm

FE - 4 point - 12.7 mm

Experiment - 4 point - 25.4 mm

FE - 4 point - 25.4 mm

Fig. 9.1.11 Comparison of experimental and simulation-predicted flexural response for 

sandwich panels subjected to 4-point bending with 12.7- and 25.4-mm core thicknesses

Figure 9.1.12 compares the experimental and predicted ultimate failure loads for 

the various sandwich beams studied in this work. The simulations-predicted 

failure loads agreed well with experiments. Also, the numerical predictions were 

slightly higher than experimental results for all cases, except for the case of 

sandwich beams with a 25.4-mm core subjected to 3-point bending. In this case, 

the failure was caused by excessive deformation of the core. Experimental 

observations revealed that the deformation in the core induced local buckling of 

the face sheet followed by compressive failure of the face sheet. These complex 

interactions, including nonlinear deformations of the core, are currently not 

simulated. Models that address these issues will eliminate the discrepancy 

observed and further increase the accuracy of the simulations. 
120

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

3-point (12.7 mm) 3-point (25.4 mm) 4-point (12.7 mm) 4-point (25.4 mm)

Type of Sandwich Panel

Failure Loads (N)

Experimental Data

Simulations

Fig. 9.1.12 Comparison of flexural failure loads from simulations and experiments

Overall, the simulation predictions agreed well with experiments in both the 

failure loads and mode of failure. These experimentally validated simulations can 

be used to further extrapolate the results beyond existing experiments. Also, the 

experimentally validated simulations can be used as a design tool and can 

eliminate the costly trial-and-error approach of developing efficient structural 

sandwich panels.

9.1.6 Conclusions

An integrated experimental and numerical program was carried out to evaluate the 

flexural properties of foam core composite sandwich panels, resin-infused by a 

single-step VARTM process. The flexural behavior for sandwich panels with 2 

different core thicknesses and foam densities was studied using 3- and 4-point 

bending tests. Finite element models simulating the tests were performed using 

accurate material properties for the cores and facing sheets. Results revealed 

excellent agreement between simulations and experiments in predicting failure 

loads. A wide range of characterization is possible, depending on the constituents, 

material layout/configuration, and testing schemes. Hence, experimentally 

validated simulations that successfully predict both the failure mode and the 

capacity, such as the ones developed in this work, can be used as a robust design 

tool to predict the properties of sandwich beams beyond the experimental matrix 

and to develop efficient, novel structural sandwich composites. A similar 

approach can be extended to other structural materials and components.
121

9.1.7 References

[1] Kabir, M.E., Saha, M.C. and Jeelani, S. (2006). “Tensile and fracture 

behavior of polymer foams.” Materials Science and Engineering A, 429: 

225–235.

[2] Sharma, S. (2010). “Process development issues of glass-carbon hybridreinforced polymer composite wind turbine blades.” Journal of Composite 

Materials, 44(4):437–456. 

[3] Williams, C., Juska, T., Duer, C. and Caiazzo, A. (2004). ”Progress in 

materials for marine composite structures.” In: Proc. International SAMPE 

Technical Conference (SAMPE Long Beach, CA). p.1845–1853.

[4] Gates, T.S., Odegard, G.M., Frankland, S.J.V. and Clancy, T.C. (2005) 

“Computational materials: Multiscale modeling and simulation of 

nanostructured materials.” Composites Science and Technology, 65:2416–

2434.

[5] Kabir, M.E., Saha, M.C. and Jeelani, S. (2006). “Tensile and fracture 

behavior of polymer foams.” Materials Science and Engineering A, 

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124

9.2 Mechanical Characterization of Glass and Carbon Fiber 

Composites Fabricated by Resin Infusion

Alfred Loos and Stephen Sommerlot

9.2.1 Summary

For use as a benchmark to compare later with novel alterations of composite 

materials, woven glass and carbon fiber composites fabricated by resin infusion 

methods were constructed and tested for basic mechanical properties. These are 

the first steps toward the ultimate goal of producing lightweight composite 

materials for protective panels in vehicles that have a high resistance to impact. 

The selected materials used were AGY’s plain weave S-2 Glass, A&P 

Technology’s Qiso-brand braided triaxial fabric, and Applied Poleramic SC-15 

epoxy resin. Resin transfer molding (RTM) and vacuum-assisted resin transfer 

molding (VARTM) are presented as means for fabrication, while basic 

mechanical properties are explored through tensile, shear, flexural, and 

compressive testing. The S-Glass test specimens were fabricated by both VARTM 

and RTM, while the carbon fiber Qiso was created using VARTM only, 

producing 3 comparable specimen types. Results generally showed that the Qiso 

produced the highest strength characteristics followed by the RTM-produced SGlass, and finally the VARTM S-Glass. The corresponding moduli—elastic, 

compressive, flexural, and shear—followed similar trends, with the Qiso 

generally having the greatest strength characteritics, followed by RTM then 

VARTM S-Glass.

9.2.2 Introduction

Protective paneling in vehicles must be produced from high-quality, high-strength 

materials. Composite materials, paired with their lightweight behavior, fill this role 

well. Using a woven glass fabric, S-Glass produced by AGY, and a triaxial-braided 

carbon fiber fabric, Qiso, produced by A&P Technology, Michigan State 

University’s Composite Vehicle Research Center produced composite materials. 

These fabrics, when infused with Applied Poleramic’s SC-15 epoxy resin through 

either resin transfer molding (RTM) or vacuum-assisted resin transfer molding 

(VARTM), were machined into test specimens so that basic mechanical properties 

could be explored. These properties, once combined with later penetration and blast 

testing, will form a benchmark to compare novel alterations of similar composite 

materials. Those novel alterations will be used to enhance desired properties of the 

composites so that the ultimate goal of producing materials for protective panels in 
125

vehicles can be optimized and validated. Data produced from such basic 

mechanical testing of tensile, shear, flexural, and compression paired with other 

literature can also serve as a basic reference for the materials’ properties.

9.2.3 Composite Design and Fabrication

9.2.3.1 Materials

As stated earlier, AGY’s S-Glass and A&P Technology’s Qiso were used as 

reinforcements in the composites fabricated. These fabrics can be seen in 

Fig. 9.2.1. S-Glass is a plain weave glass fiber material, while Qiso is a triaxialbraided carbon fiber fabric. Table 9.2.1 shows a direct comparison of the S-Glass 

and Qiso’s basic properties.

Fig. 9.2.1 (Left) AGY’s S-2 Glass and (right) A&P’s Qiso

Table 9.2.1 S-glass and Qiso basic fabric properties

S-Glass Qiso

Material Glass fiber Carbon fiber

Fiber orientation Plain weave 0, ±60°

Thickness (inches) 0.03 0.021

Areal weight (g/m2) 815 531

The matrix to pair with these reinforcements was chosen to be Applied 

Poleramic’s SC-15 epoxy resin. This is a 2-phase epoxy cycloaphatic amine with 

a viscosity at ambient temperature of 350 centipoise. 

9.2.3.2 Preform Layup

Before resin infusion could begin, the reinforcement’s material was cut to size 

and oriented in a specific layup. Composites were fabricated in panels that were 

either 12 × 12 inches or 12 × 24 inches. The S-2 Glass, because of its plain weave 

fiber orientation, was stacked in a quasi-isotropic 9-layer layup. This layup is 

illustrated in Fig. 9.2.2. The Qiso’s triaxial-braided 0, ±60° fiber orientation offers 
126

equal material weight in all directions, which provides an initial quasi-isotropic 

characteristic. A 4- or 6-ply-thick layup was then used, where the 0° fibers were 

stacked in line. A 4-layer-thick Qiso panel was used for tensile testing because of 

the material’s high strength, while a 6-ply layup was used for the remaining tests. 

Fig. 9.2.2 Quasi-isotropic layup for S-glass

9.2.3.3 Fabrication

In the VARTM process, either an S-2 Glass or Qiso preform was sealed on a 

plate, sandwiched by peel plies, and covered by an infusion medium underneath a 

vacuum bag. Pulling down to full vacuum, we introduced resin at an inlet at one 

end of the preform and pulled through to the exit at the opposite end. This method 

creates a quick, fairly compressed full resin infusion. Continued under vacuum, 

the plate holding the composite was placed in a curing oven for a 6-h cure cycle. 

This cycle complies with the manufacturer’s recommended curing process, which 

includes 2 h of oven time at 60 °C and then 4 h at 94 °C. Figure 9.2.3 shows a 

VARTM setup where the preform, covered in infusion medium (red material) and 

peel plies (blue material), is sealed under vacuum as resin advances through the 

fibers. Tacky tape (yellow) surrounds the setup as a means for the vacuum bag to 

seal.

Fig. 9.2.3 VARTM setup

The RTM process, which was only used with the S-Glass, is a compressive 

molding procedure. The quasi-isotropic S-2 Glass preforms were placed in a 

specially designed mold where resin could be introduced and then the mold could 

be compressed until a full cure was achieved. To accomplish this, a TMP 140-Ton 
127

vacuum hot press was employed to clamp the mold and then heat it with the same 

6-h cure cycle used in the VARTM process, as SC-15 resin was continued to be 

used as the matrix. With the mold under minimal pressure, resin was introduced 

to the preform through an inlet at the top center of the mold via air pressure from 

a pressure pot. Once full infusion was achieved, as resin exits from the 2 outlet 

holes on the mold’s sides, the TMP press was used to clamp with sufficient force 

to fully close the mold, and the curing cycle could begin. This process compacts 

the S-2 Glass fibers greatly, and that difference, compared to VARTM, is evident 

in test results. Figure 9.2.4 displays an RTM setup. 

Fig. 9.2.4 RTM setup

9.2.4 Experimental Techniques

9.2.4.1 Tensile Testing

Tensile testing was conducted in accordance with ASTM D3039 procedures. 

VARTM S-Glass specimens were tested straight-sided with no tabs, while the 

Qiso and RTM S-Glass specimens, because of their higher-strength 

characteristics, required end tabs to create successful gage length failures. Initial 

untabbed trials resulted in slipping or failure under the grips. The tabs for the Qiso 

coupons were created from 6-ply Qiso composite pieces in accordance with 

ASTM tab recommendations (roughly 2 inches in length) and adhered with 

Loctite Super Glue Gel Control. The Qiso test coupons were machined from a 

4-layer Qiso VARTM panel to ensure that the testing frames could provide 

sufficient load for specimen failure. The tabs for the RTM S-2 Glass samples 

were composed of a thinner fiberglass material. Tests were run at a speed of 

5 mm/min until failure, so both the tensile strength and elastic modulus could be 

attained. All coupons had their 0° fibers running along the length of each 

specimen. Strain for all tests was calculated with the aid of EIR’s model LE-05 
128

laser extensometer with a laser gage length of 50 mm. The results and 

comparisons of these tests are found in the Results section. The setup of this test 

can be seen in Fig. 9.2.5.

Fig. 9.2.5 Tensile test setup

9.2.4.2 Compressive Testing

The 3 material types were tested for compressive properties using ASTM 6641 

and a Combined Loading Compression (CLC) test fixture from Wyoming Test 

Fixtures. The fixture was loaded into a 100-kN screw-driven MTS Insight 

between 2 compression platens. This setup can be seen in Fig. 9.2.6. All samples 

were straight sided and untabbed. All coupons were machined to be 12 mm wide 

by 140 mm long, allowing for a 13-mm (~0.5 inch) gage length. The specimens 

were cut so that the 0° fibers were positioned vertically with the coupon lengths. 

Tests were conducted with a crosshead speed of 0.05 inches/min and until failure 

occurred. Strain calculations were made from data acquired by the LE-05 laser 

extensometer with a laser gage length of 70 mm. Laminate compressive strength

and modulus were calculated from these data. The compressive modulus was 

measured over a range of axial strain of 1,000 to 3,000 microstrain. All coupons 

produced acceptable gage length failures, and their results are found in the Results 

section.
129

Fig. 9.2.6 Compression test setup

9.2.4.3 Shear Testing

Two major tests were used to define shear properties for the composite materials 

tested. The V-Notched Beam Method or Iosipescu Shear Method (ASTM D5379) 

was used for both the VARTM- and RTM-produced S-2 Glass, while the Qiso 

material was tested with the V-Notch Rail Shear Method (ASTM D7078). Both 

tests used fixtures produced by Wyoming Test Fixtures. In initial Iosipescu tests, 

Qiso samples would experience crushing at the loading points before gage length 

failure occurred, so a different test (V-Notch Rail Shear) was needed that could 

handle the high-strength specimens. The Iosipescu coupons were 20 mm thick by 

76 mm long with 90° notches at the center allowing for a 12-mm gage length. The 

coupons were cut so that the 0° fibers were in line with the length of the 

specimen. The Iosipescu specimens were tested at 0.05 inches/min with a 100-kN 

MTS Insight until failure occurred. To acquire strain data, strain gages were 

adhered in line with the gage length of each coupon. Strain at ±45° orientations 

were measured on the rosettes so that shear strain could be calculated by adding 

the absolute value of strain in both orientations. The RTM S-Glass specimens 

were tested with 120-ohm strain gages, but because of exhausted supplies, 

350-ohm gages were ordered to finish with the VARTM S-2 Glass samples. The 

ultimate shear strength and the shear modulus of elasticity were calculated for. 

The shear modulus was observed over a 4,000 ±200 microstrain range with the 

lower strain point inside the 1,500 to 2,500 microstrain range. As for the Qiso 

samples, a larger coupon was used: 56 mm wide by 76 mm long. Similar to the 

Iosipescu coupons, each V-Notch Rail specimen had 90° notches cut at the center 
130

of the specimen so that a 31-mm gage length through the center of the sample was 

achieved. We adhered 350-ohm strain gages to these specimens so ±45° strain 

orientations could be measured to later calculate for shear strain. The shear 

modulus (chord modulus) was calculated using the same stipulations as the 

Iosipescu tests. The coupons were loaded into the Rail Shear fixture with the 

gripping bolts torqued to the recommended 40 ft-lb using a torque wrench. A 100-

kN screw-driven MTS Insight was employed, while specimens were tested with a 

crosshead speed of 2 mm/min until gage length failure occurred. The setups for 

Iosipescu and Rail Shear tests can be seen in Figs. 9.2.7 and 9.2.8, respectively.

Fig. 9.2.7 Iosipescu test setup
131

Fig. 9.2.8 Rail shear test setup

9.2.4.4 Flexural Testing

Flexural tests were conducted in adherence to ASTM 6272 procedures for 4-point 

bending. Both RTM and VARTM S-2 Glass samples were tested using a load 

span of one-third of the support span distance. The Qiso coupons were tested with 

a load span of half the support span and with a higher span-to-depth ratio, 40:1, in 

order to decrease shear deflections so that the apparent modulus would not be 

reduced. The LE-05 laser extensometer was used to measure strain as coupons 

were tested to failure to observe both the flexural strength and the flexural 

modulus. Flexural modulus was defined as the slope of the elastic portion of the 

materials’ stress-strain curves. Specimens were tested at crosshead speeds 

calculated from the ASTM depending on the load span to support span ratio and 

the span-to-depth ratio. The Qiso, for instance, was tested at 8 mm/min, faster 

than the S-2 Glass coupons because of its higher span-to-depth ratio. Figure 9.2.9 

displays the flexural test setup.
132

Fig. 9.2.9 Flexural test setup

9.2.5 Results

All experiments were completed with at least 5 specimens tested, in adherence to 

general ASTM procedure. If significant deviation from the average existed or 

inconsistency during a specific test occurred, a data set could be deemed 

unrepresentative of its corresponding test group. The results from all tests, 

ultimate strengths and moduli, were averaged and presented graphically. The 

tensile test results for ultimate tensile strength and elastic moduli can be seen in 

Figs. 9.2.10 and 9.2.11, respectively. Note that all error bars represent 1 standard 

deviation of the data above and below the average. Numerical representation of 

the data then follows in Table 9.2.2.
133

Fig. 9.2.10 Average ultimate tensile strength comparison

Fig. 9.2.11 Average elastic modulus comparison
134

Table 9.2.2 Tensile test results

Material Qiso RTM S-2 Glass VARTM S-2 Glass

Property

Tension Tension Tension

Modulus 

(GPa)

Strength

(MPa)

Modulus

(GPa)

Strength

(MPa)

Modulus 

(GPa)

Strength

(MPa)

Average 44.14 786.17 20.06 383.65 15.69 323.89

STD 2.64 34.93 3.61 11.98 0.6 3.29

CV (%) 5.98 4.44 17.98 3.12 3.85 1.02

Results from compressive testing now follow in Figs. 9.2.12 and 9.2.13, where the 

average laminate compressive strengths and laminate compressive moduli are 

displayed. Table 9.2.3 shows the numerical averages and other statistics of these 

results.

Fig. 9.2.12 Average laminate compressive strength comparison
135

Fig. 9.2.13 Average laminate compressive modulus comparison

Table 9.2.3 Compression test results

Material Qiso RTM S-2 Glass VARTM S-2 Glass

Property

Compression Compression Compression

Modulus 

(GPa)

Strength 

(MPa)

Modulus 

(GPa)

Strength 

(MPa)

Modulus 

(GPa)

Strength 

(MPa)

Average 42.23 379.09 31.83 258.60 21.66 240.78

STD 6.68 25.92 3.75 23.99 2.57 4.31

CV (%) 15.81 6.84 11.78 9.28 11.87 1.79

The Iosipescu and V-Notch Rail Shear strength test results are shown in Fig. 

9.2.14. Note that these results include the RTM and VARTM S-Glass samples 

from Iosipescu tests and Qiso data from the V-Notch Rail Shear Test Method. 

Following in Fig. 9.2.15, the shear chord moduli for each material can be found 

while the method employed for testing is also noted. Table 9.2.4 displays the 

numerical data represented in the figures.
136

Fig. 9.2.14 Average failure shear strength from the Iosipescu and V-Notch rail shear tests 

methods

Fig. 9.2.15 Average shear moduli from the Iosipescu and V-Notch rail shear test methods
137

Table 9.2.4 Shear test results

Material Qiso RTM S-2 Glass VARTM S-2 Glass

Property

Shear Shear Shear

Modulus

(GPa)

Strength 

(MPa)

Modulus

(GPa)

Strength

(MPa)

Modulus

(GPa)

Strength

(MPa)

Average 10.02 234.61 5.62 97.43 3.74 108.19

STD 0.79 11.29 0.5 7.92 0.69 2.55

CV (%) 7.86 4.81 8.97 8.13 18.36 2.36

The final set of results is from the flexural testing. Figures 9.2.16 and 9.2.17

display the average flexural strength and flexural modulus of elasticity from each 

material type tested. Table 9.2.5 displays the numerical data represented in the 

figures.

Fig. 9.2.16 Average flexural strength comparison
138

Fig. 9.2.17 Average flexural modulus comparison

Table 9.2.5 Flexural test results

Material Qiso RTM S-2 Glass VARTM S-2 Glass

Property

Flexural Flexural Flexural

Modulus

(MPa)

Strength

(MPa)

Modulus

(GPa)

Strength

(MPa)

Modulus

(GPa)

Strength

(MPa)

Average 28.47 341.11 16.23 334.61 16.48 309.2

STD 4.07 43.28 0.45 17.5 1.03 17.11

CV (%) 14.31 12.69 2.79 5.23 6.26 5.53

9.2.6 Summary

Upon examination of the test results, a few general trends can be observed. The 

Qiso material and its coupons generally appear to be the overall strongest while 

exhibiting most of the highest ultimate strengths and highest moduli. The RTMproduced S-Glass exhibits the next highest strengths and moduli, followed by the 

VARTM S-Glass. This trend seen in the S-Glass specimens is because RTMproduced panels use a much greater compaction force when infused than the 

VARTM panels. As pressure increases so does the fiber volume fraction, giving 

the RTM S-Glass more fibers to resist external forces in each coupons’ area than 

the VARTM samples. The Qiso’s carbon fiber triaxial braided nature high 

strengths in comparison to the S-Glass were evident throughout testing, especially 
139

when separate shear testing methods were needed, as well as greater span-todepth ratios during flexural testing. Blast and impact testing to investigate fiber 

breakage will be needed to further pursue validation for vehicle implementation. 

9.2.7 Bibliography

“Standard Test Method for Flexural Properties of Unreinforced and Reinforced 

Plastics and Electrical Insulating Materials by Four-Point Bending,” ASTM 

Standard D6272, 2010

“Standard Test Method for Tensile Properties of Polymer Matrix Composite 

Materials,” ASTM Standard D3039, 2008

“Standard Test Method for Compressive Properties of Polymer Matrix 

Composite Materials Using a Combined Loading Compression (CLC) Test 

Fixture,” ASTM Standard D6641, 2009

“Standard Test Method for Shear Properties of Composite Materials by the VNotched Beam Method,” ASTM Standard D5379, 2012

“Standard Test Method for Shear Properties of Composite Materials by the VNotched Rail Shear Method,” ASTM Standard D7078, 2012
140

9.3 Design and Manufacture of a High-Mobility Multipurpose 

Wheeled Vehicle (HMMWV) Component

Alfred C Loos and Adam C Klein

9.3.1 Summary

The overall objective of this project was to identify, design, and manufacture a 

door or component of similar complexity for a HMMWV. This report focuses on 

the selection, design, and manufacture of a rear bumper support strut.

9.3.2 Introduction

The main objectives of the work package were as follows:

• To identify a component of the vehicle that would be suitable for design 

and manufacture using composites

• To design and manufacture a component of the HMMWV using 

composites

• To work with other thrust areas to demonstrate Composite Vehicle 

Research Center (CVRC)-developed technologies

This report will provide detailed information on the approach and the work 

completed on this project.

9.3.3 Component Identification

Initially 2 parts of the vehicle were identified for potential use in this project: the 

rear bumper assembly and the tailgate. Figures 9.3.1 and 9.3.2 show the tailgate 

and the rear bumper assembly, respectively, after they had been removed from the 

vehicle.
141

Fig. 9.3.1 Tailgate

Fig. 9.3.2 Rear bumper assembly
142

Based on recommendations by the sponsor and the potential to incorporate

CVRC-developed sensing technologies, we selected the rear bumper assembly as 

the component to focus on for the project. With the rear bumper assembly, we 

could incorporate advanced and multimaterial joining applications. The bumper 

assembly was also seen as a possible location for the incorporation of 

multifunctional materials.

Because of the level of complexity of the bracketry on the rear bumper assembly,

we decided that the focus would be on a single strut piece. The strut piece used is 

shown in Fig. 9.3.3.

Fig. 9.3.3 Rear bumper strut

The bumper strut was removed from the rear bumper assembly and used as the 

mold for the fabrication of the composite bumper strut. The strut was about 

26 inches long and 4 inches wide. The strut weighed 8 lb, but the composite strut 

was predicted to weigh significantly less.

9.3.4 Materials and Process

The mold in which the preform was draped was the original steel bumper strut 

that had been removed from the vehicle. A high-density closed-cell PVC foam 

was fitted into the channel of the steel strut. This fitting allowed for the vacuum 

bag to be sealed around the edges and ends of the steel mold.

The preform comprised 6 layers of an S-2 Glass fabric with an 8-harness satin 

weave supplied by US Composites. This weave pattern allowed for enough 

flexibility in the preform so that it could conform to the different curvatures of the 

mold. The 6 layers were stitched together using the Laystitch stitching machine. 

The preform was draped over the mold, and the corners were then notched to 

avoid wrinkling in the corners. Figure 9.3.4 shows the fabric and the cut shape of 

the preform.
143

Fig. 9.3.4 Fabric and cut preform

Where the fabric was slit to conform to the inside corners in the mold, the preform 

tended to open up and expose the mold in these areas, creating spots that were 

lacking in reinforcement. To solve this problem, thin filler strips of fabric were 

inserted between each layer of fabric to reinforce these inside corners. 

After the fabric had been draped over the mold, a peel ply, resin transfer medium, 

and vacuum bag, respectively, were placed on top of the preform. The vacuum 

bag was then sealed around the sides and ends of the mold to prevent resin flow 

channels from occurring. 

After the vacuum bag was sealed around mold, the bag was then sealed with 2

layers of sealant tape to the tooling plate. This seal ensured that a satisfactory 

vacuum pressure was able to be achieved prior to the resin infusion. Figure 9.3.5

shows the draping of the preform over the steel mold while Fig. 9.3.6 shows the 

final layup after all of the layers have been sealed under the vacuum bag.

Fig. 9.3.5 Preform draping
144

Fig. 9.3.6 Final layup stage

The vacuum-assisted resin transfer molding (VARTM) process was used for the 

fabrication of the composite bumper strut. In this process a fabric preform is 

infused with an Applied Poleramic SC-15 2-phase epoxy resin and oven cured. 

The curing cycle consisted of curing at a temperature of 60 °C for 2 h and then 

curing for an additional 4 h at 94 °C. Figure 9.3.7 shows the VARTM setup used 

for the infusion of the strut preform.

Fig. 9.3.7 VARTM setup
145

9.3.5 Trials

Several fabrication trials were performed, with improvements being made after 

each trial leading to better results.

For the first trial, the vacuum bag was sealed to the tooling plate but not around 

the mold or the preform. Thus, during the infusion, resin flowed around the edges 

of the mold and flowed between the mold and the foam core. This led to 

inconsistent saturation of the preform as well as the foam core becoming covered 

with resin. Figure 9.3.8 shows trial 1 after infusion. It can clearly be seen from the 

picture that flow channels developed around the mold and that the resin flowed 

under the mold and down the sides of the foam core.

Fig. 9.3.8 Trial 1 after infusion

Based on the results from the first trail, several improvements were made for trial 

2. Trial 2 was infused with oil that had a similar viscosity to that of the SC-15 

epoxy resin. Also, before the infusion of trial 2, the vacuum bag was sealed 

around the sides and ends of the mold. This seal was done to prevent the resin 

from running under the steel mold and from creating flow channels around the 

side of the mold. The infusion was successful, and thorough saturation of the 

preform was achieved.

For the third trial, minor changes were made to improve the results even more. 

The filler strips described earlier were added to the inside corners for 

reinforcement. For this trial SC-15 resin was used for the infusion instead of the 

oil used for the infusion in trial 2. The resin was also oven cured after infusion for 

trial 3.
146

9.3.6 Results

The outcome of this project yielded several important results. An efficient 

manufacturing technique was developed for the fabrication of composite parts 

with complex shapes using the VARTM process. Also, a successful prototype of 

the rear bumper strut was fabricated from composites. Figure 9.3.9 shows a 

photograph of the completed bumper strut prototype.

Fig. 9.3.9 Composite bumper strut prototype

The results of this project also helped pinpoint the areas of composite 

manufacturing of complex geometries that need improvement. An improved 

vacuum bagging technique needs to be used to reduce wrinkles on the surface of 

the finished part. In Fig. 9.3.10 a wrinkle in the outer surface of the part can be 

seen above the recessed inside corner area of the part inside the red border.
147

Fig. 9.3.10 Highlighted wrinkle in part surface

Because of the difficulties that were discovered in removing the part from the 

steel mold, an improved release mechanism needs to be incorporated into the 

process. The use of tackified fabrics may also be beneficial to reduce the sliding 

of the various layers during the layup process and to help reduce fraying of the 

fabric preform.
148

9.4 Interlaminar Reinforcement of Glass Fiber/Epoxy 

Composites with Graphene Nanoplatelets

Nicholas T Kamar, Mohammad Mynul Hossain, Lawrence Drzal, and 

Alfred Loos

9.4.1 Summary

Incorporation of carbonaceous nanomaterials into fiber-reinforced/epoxy polymer 

composites (FREPCs) has been shown to improve a variety of intralaminar 

mechanical properties. Our research investigated the ability of graphene 

nanoplatelets (GnPs) to improve the interlaminar properties of glass-reinforced 

multilayer composites. We developed a novel method for the inclusion of GnPs 

into plain-weave glass fabric FREPCs processed with vacuum-assisted resin 

transfer molding (VARTM). Pristine GnPs are dispersed in a solvent solution of 

diglycidyl ether of bisphenol A epoxy resin and then uniformly coated onto the 

surface of glass fabrics at different concentrations prior to laminate stacking. The 

sizing/GnP combination adheres to the glass fabric and allows full resin infusion 

using a conventional VARTM processing method. Subsequently, 4-point bending 

flexural test results on the cured laminate produces a 29% improvement in 

flexural strength with the addition of only 0.25 wt% GnP compared to the pristine 

glass FREPCs. At the same loading, mode-I fracture toughness testing revealed a 

25% improvement. Additionally, low-velocity drop weight impact testing 

indicated improved energy absorption capability with increasing interlaminar

concentration of GnPs. Qualitatively, ultrasonic c-scans and dye penetration 

inspection analysis supports these results. 

9.4.2 Introduction

Recently, in an effort to reduce carbon dioxide emissions, the Environmental 

Protection Agency (EPA) and the National Highway Traffic Safety Administration 

(NHTSA) have introduced strict fuel economy standards for light duty vehicles [1]. 

Light-weighting vehicles with high-performance fiber-reinforced epoxy polymer 

composites (FREPCs) will likely play a major role in continuing to meet or exceed 

EPA and NHTSA standards. Compared to steel, FREPCs offer very high specific 

strength and stiffness at a low density. However, FREPCs are brittle materials, 

owing to their cross-linked polymer structures and strong and stiff glass fibers that 

facilitate their high strength and modulus [2]. Therefore, over the last 4 decades, 

researchers have investigated the incorporation of micron and, more recently,

nanoscale materials to improve FREPC toughness [3].
149

Because of their outstanding thermal, electronic, and mechanical properties, 

carbonaceous nanomaterials have shown considerable promise as additives to 

epoxy polymer composites (EPCs) [4] and FREPCs [5] in improving a variety of 

composite mechanical properties. These materials include carbon nanotubes 

(CNTs), buckminsterfullerene (C60), and, more recently, GnPs [4,6]. In particular, 

CNTs as additives to polymer matrices have been explored extensively [7]. Their 

high aspect ratios and high specific strength and stiffness have been shown to 

improve strength, stiffness, and toughness of EPCs and FREPCs; of course, their 

beneficial effects are contingent on the degree of their dispersion throughout the 

composite matrix [8]. Chemical functionalization of CNTs can provide enhanced 

dispersion and improved interfacial interaction between the polymer matrix and 

functional nanomaterial [7]. 

Indeed, the beneficial incorporation of CNTs into EPC matrices has been 

demonstrated. However, CNTs are expensive, available at a cost of about 100 US 

dollars per gram [9]. Therefore, to enhance EPC properties in a cost-effective 

manner, researchers have investigated the incorporation GnPs, which share 

CNT’s outstanding thermal, electronic, and mechanical properties at about 

1/500th of the cost [10]. These materials are conceptually different from graphene 

oxide, which is produced via an oxidative process that disrupts the sp2 basal plane 

hybridization, effectively reducing graphene’s outstanding properties; reduced 

graphene oxide shares these effects [11–13]. Furthermore, GnPs share nanoclay’s 

layered structure in which graphene sheets are held together via weak van der 

Waals dispersive forces. As such, GnPs can be intercalated and exfoliated 

throughout an EPC matrix. 

Two primary methods to introduce nanoparticles into laminate composite 

materials made via resin transfer molding (RTM) are described in the literature. 

One method involves dispersing the nanoparticles into a resin system, followed by 

nanoenhanced resin infusion of the laminate plies. However, the high surface area 

and aspect ratio of the nanoparticles can result in significant increases in resin 

viscosity [14]. Furthermore, filtration of the nanoparticles at the resin inlet by the 

fiber reinforcement can prevent a homogeneous dispersion. Another method 

involves coating/sizing the nanoparticles directly onto the fiber surfaces [15], 

followed by resin infusion, which can eliminate problems created with the first 

technique. 

Indeed, researchers have investigated the strengthening and toughening 

mechanisms of graphene in epoxy polymer nanocomposites [16,17]. However, to 

our knowledge, the current literature does not describe the addition of pristine 

GnPs in the interply regions of FREPCs produced via VARTM. As such, this 

work will investigate the dispersion of GnPs onto the surface of plain-weave glass 
150

fabric prior to infusion with a 2-part diglycidyl ether of bisphenol A epoxy resin 

system. The influence of interply GnP concentration on nanocomposite flexural 

properties, mode-I fracture toughness, and low-velocity impact properties will be 

described.

9.4.3 Methods

9.4.3.1 Materials

A low-viscosity 2-part resin system—namely, Applied Polymeric SC-15 epoxy 

resin (SC-15A) and SC-15 amine hardener (SC-15B)—was used in both the 

coating and VARTM processing. The fiber reinforcement was Owens-Corning 

ShieldStrand S-2 Glass in plain-weave with an areal-specific weight of 818 g/m2. 

Exfoliated graphene nanoplatelets (xGnP-5) with a mean diameter of 5 μm and a 

thickness of 7 nm were kindly provided by XG Sciences in Lansing, Michigan.

9.4.3.2 Coating Process

To disperse the GnPs into a solvent for subsequent coating onto glass fabric, 

measured quantities of GnPs were added to a solution of 30 g of SC-15A, 2.25 g 

of SC-15B, and 500 mL of 2-isopropanol. The mixture was then stirred for 5 min, 

followed by tip sonication at 35% amplitude for 1 h. After sonication, aliquots of 

solution were weighed and brushed onto the surface of S-2 Glass fabrics such that 

the final composite laminate panels had concentrations of 0.10, 0.25, 0.50, and 

1.0 wt%, respectively. The coated fabrics were degassed in a fume hood for 12–

15 h prior to infusion.

9.4.3.3 Composite Production

Glass fabrics with dimensions of 30.48 by 30.48 cm (for flexural and fracture 

toughness test specimens) and 60.96 by 30.48 cm (for impact test specimens) 

were infused with the SC-15 resin system using the VARTM process, where the 

ratio of SC-15 A:B was 100:30 by weight. A 4-ply layup was used to manufacture 

flexural and impact test specimens, while 8-ply layups were used to produce 

fracture toughness test specimens. Further, a 140-mm impervious Teflon peel-ply 

material, which spanned the length of the panel, was folded in half and placed 

between the fourth and fifth plies of the fracture toughness test specimens. The 

infusion system consisted of a 91.44- by 60.96-cm steel tooling plate with a single 

injection port and a single vacuum port. The layup consisted of a tooling plate, a 

small quantity of liquid mold release, coated (as described in 9.4.3.2) or pristine 

glass fabric, release ply material, and distribution media. The layup was vacuum
151

bagged under 0.2 kPa, and the infusion was completed within 2 to 4 min. The 

infusion system was then placed into a convection oven for cure at 60 °C for 2 h 

and postcure at 94 °C for 4 h. 

9.4.4 Testing

9.4.4.1 Flexural Testing

Four-point bending flexural specimens were prepared and tested in accordance 

with ASTM D-6272, the test method for flexural properties of unreinforced and 

reinforced plastics by 4-point bending. Sample testing was performed on a 

Materials Test System (MTS) 810 in conjunction with a laser extensometer to 

accurately measure sample displacement. The coupons were 12.7 × 60 mm with 

an average thickness of 2.60 mm. Each coupon was carefully placed onto the 

support bars to ensure symmetric and level sample loading. The load span was 

taken as one-third of the support span, and the samples had a 16:1 span-to-depth 

ratio. A 20-N preload was applied at the beginning of each flexural test. A

1.215-mm/min crosshead speed, as calculated from the support span and sample 

thickness, was applied to each specimen. The specimens were tested until failure,

and their load-displacement data were recorded for analysis. 

9.4.4.2 Fracture Toughness Testing

Eight-ply panels with a folded 70-mm impervious Teflon peel-ply material insert 

located between the fourth and fifth middle plies were manufactured for mode-I 

testing. Specimens were prepared and tested on the basis of ASTM 5528, the test 

method for mode-I interlaminar fracture toughness of unidirectional fiberreinforced polymer matrix composites, except that the piano hinges were rotated 

180° for simpler attachment. Since the standard calls for a 50-mm insert, 20 mm 

of the 70-mm initial insert and panel edge were removed with a diamond-tip saw. 

Piano hinges with a width of at least 25.4 mm were attached with a 5-min cure 

Quickset epoxy to the ends of the 25.4-mm-wide and 152.4-mm-long samples. 

The average sample thickness was 4.85 mm. A clean razor blade was then 

carefully used to remove the starter crack Teflon material, and the sample was 

placed onto the MTS grips for testing. A constant loading rate of 5 mm/min was 

applied to open the samples under mode-I, and a Cannon EOS Rebel T4i was 

used to record the crack event. The drawing program Corel was used to calculate 

the length of the crack from the end of the starter crack to the initial onset of 

failure. Mode-I fracture toughness (GIc) values were calculated according to 

modified beam theory, as shown in Eq. 9.4.1.
152

, (9.4.1)

where P is the load (newtons), δ is the load point displacement (meters), b is the 

specimen width (meters), and a is the delamination length (meters). 

9.4.4.3 Low-Velocity Impact Testing

A Drop-Weight Instron Dynatup 8250 impact machine was used for impact 

testing. The machine setup consists of a 12.7-mm-diameter instrumented tup 

mounted on a crosshead with a provision for attachment of weights. To perform 

an impact test, the 11.82-kg impactor is released along stiff, smooth guide 

columns and singularly impacts a mounted composite sample. The 12.7- ×

12.7-cm samples were clamped at the base of the test fixture by 8 bolts. The 

energy of impact is governed by the drop height; 4 different energy levels were 

used for testing: 20, 40, 60, and 80 J, respectively. Load displacement curves were 

recorded for analysis.

9.4.4.4 Dye-Penetration Inspection

Dye penetration inspection (DPI) was performed to investigate the surface 

damage caused by a low-velocity impact event, which was not visible to the 

naked eye. Zyglo Penetrant ZL-27A fluorescent inspection dye was sprayed onto 

the impact surfaces and back sides of the impacted samples. After 30 min, excess 

penetrant was removed, and Zyglo Developer Z9-9F was added to provide 

contrast. Ultraviolet light was then applied to each side of the sample, and 

fluorescence was captured by a digital camera.

9.4.4.5 Ultrasonic C-Scanning

To evaluate the internal structure of the impacted specimens, ultrasonic 

C-scanning was performed using the Pulse-Eco method. An ultrasonic 

ULTRAPAC II system (automated immersion system), in association with 

UTwinTM software, was used to perform data acquisition, imaging, and analysis. 

The scanning rate was 5 MHz, and a 12.70-mm-diameter transducer was used. To 

perform ultrasonic inspection, the sample was placed in a water tank and the 

transducer was brought over the sample. The scan area was selected by choosing 

the x and y position that would result in complete scanning of the specimen. As 

the ultrasound propagated through the water medium, a portion is reflected back 

from the top surface of the sample, while the rest of the sound passes through the 

material. Defects present in the FREPC act as reflectors, and a defect echo is 

obtained. To evaluate the reflected signal amplitude changes through the back of 

each specimen, an electronic gate was set on the back surface echo to digitize the 
153

signal. For each impacted specimen, scanning was carried out for both the front 

(impact side) and back surface facing the transducer. The digitized data were

further analyzed by pseudo-coloring to get a colored map to differentiate a 

defective area from a “good” area. 

9.4.5 Results and Discussion

9.4.5.1 Mechanical Properties

The stress-strain behavior of a single 4-point flexural test at GnP concentrations 

of 0 (“pristine”), 0.10, 0.25, and 0.50 wt% specimens is shown in Fig. 9.4.1. The 

slope of the elastic region is fairly linear until the first load drop; this point was 

used to calculate the maximum flexural strength per sample. The largest 

maximum flexural strengths were observed for samples with low loadings of 

GnPs—namely, 0.10 and 0.25 wt%. Once the concentration of GnPs reached 

0.5 wt%, the specimen stress-strain response closely resembled that of the pristine 

samples. These trends were observed for all samples tested. 

Fig. 9.4.1 Four-point flexural test results for pristine and 0.10, 0.25, and 0.50 wt% GnP 

samples

The average maximum flexural strength of 0, 0.10, 0.25, 0.50, and 1.0 wt% GnP 

specimens, along with corresponding error bars, is shown in Fig. 9.4.2. Pristine 

specimen flexural strength values are slightly larger than those reported in the 

literature for the S-2 Glass/SC-15 system: Haque et al. [18] determined an 

average value of 298 MPa, and we report a value of 324.78 ± 31.62 MPa. In the 

system described here, the GnP concentration was optimized at 0.25 wt%, 

resulting in a 29% increase in flexural strength compared to the pristine 
154

specimens. At 0.50 wt% GnP, the nanocomposite flexural strength showed a less 

dramatic increase. At 1.0 wt% GnP, the error bars between the pristine and GnP 

samples overlap, and no improvement is observed. Furthermore, these 

enhancements in flexural strength are comparable to those reported by Haque et 

al., but at one-fourth wt% of nanofiller; it appears that GnPs are more efficient 

nano-additives than intercalated and phase-separated montmorillonite clay in the 

S-2 Glass/SC-15 system. Additionally, the concentration of GnP had an effect on

the failure mode of the samples during flexural testing. Those samples with lower 

concentrations of GnPs (0.10 and 0.25 wt%) failed on the tension side, while 

those samples with higher concentrations of GnP (0.50 and 1.0 wt%) failed on the 

compression side or by delamination. 

Fig. 9.4.2 Average maximum flexural strength of pristine and 0.10, 0.25, and 0.50 wt% GnP 

samples

Mode-I fracture toughness (GIc) testing results for pristine, 0.25 wt%, and 1.0 

wt% GnP specimens, and their corresponding error bars, are shown in Fig. 9.4.3.

GIc was optimized at 0.25 wt% GnP loadings, yielding a 25% improvement 

relative to the pristine samples. However, the 1.0 wt% GnP specimens indicated a 

substantial decrease in GIc values, which could be due to agglomeration of GnPs 

at the fiber/matrix interface. In addition to acting as stress concentrators, such 

aggregates can form a path for crack propagation [19]. 
155

Fig. 9.4.3 Mode-I fracture toughness (GIc) results of pristine, 0.25 wt%, and 1.0 wt% GnP 

samples

Mode-I fracture surfaces of specimens with varying concentrations of GnPs were 

analyzed under scanning electron microscopy (SEM). A variety of details are 

visible at both low and high magnifications of pristine and GnP-doped samples. 

For all specimens, the SEM images indicated that the propagating crack was 

limited to the region between the middle plies. 

RTM processes, in which a fabric preform is infused with a 2-part resin system, 

can introduce voids which are most likely due to mechanically entrapped air [20]. 

Infused fabrics in plane-weave have 2 regions in which voids can form: the 

smaller region between the individual fibers within a tow and the larger region 

between individual tows where the warp and fill cross over one another; voids 

formed in these regions are called “tow voids” and “channel voids”, respectively 

[20]. 

Channel voids were predominately located at the warp and fill crossover regions,

as determined by inspection of the fracture surfaces. One such void is shown in 

Fig. 9.4.4a; this void has a diameter on the order of 1 mm. Figures 9.4.4b and 

9.4.4c show progressively higher magnification views of the color mapped boxed 

areas. The wavy surface pattern in Fig. 9.4.4c indicates a free surface. In addition 

to larger-scale channel voids, submicron scale voids are scattered throughout the 

polymer matrix. These submicron scale voids could be due to phase separation 

followed by cavitation of the toughening agent present in the Applied Polymeric 

SC-15 resin system. During cure, the epoxy resin will cross-link via a ringopening mechanism in which partially positive carbon atoms on the epoxide rings 

are attacked by the nucleophilic amine groups of the hardener; this exothermic 
156

curative process is governed by the enthalpy term in the Gibb’s free energy of 

mixing [21]. Meanwhile, the proprietary toughening agent in SC-15 can phaseseparate from the mixture [21–23]. During fracture of the cured specimen, the 

thermoplastic particles stretch and break. Once the fracture is complete, the 

particles will fall back into expanded cavities, which appear as submicron and 

micron-scale voids [24]. These submicron-scale voids were also present in the 

samples containing GnPs and look quite similar to those cavities present in the 

fracture surfaces of rubber-toughened epoxy systems [25].

Fig. 9.4.4 SEM image of a pristine mode-I fracture surface. Image a) shows a macrovoid,

and images b) and c) are progressively higher magnifications of a void edge, as indicated by 

the colored boxes.

Samples containing interply dispersed GnP exhibit different fracture surface 

morphologies at the warp and fill crossover regions. For example, Fig. 9.4.5a

shows that the channel regions in the 0.25 wt% GnP specimens have a rougher 

edge morphology than those same regions in the pristine specimens. Figure 9.4.5b 

shows a closer view of the 0.25 wt% GnP mode-I fracture surface fibers. GnPs 

present on these surfaces appear to be aggregated to some extent. Figure 9.4.5c 

shows an intertow aggregate of GnP-5, which should have an average platelet 

diameter of 5 μm. This platelet appears to have a basal plane larger than the mean 

diameter.

 a) b) c)

Fig. 9.4.5 Mode-I fracture surface of a 0.25 wt% GnP sample. Image a) shows a typical 

channel region, image b) is a closeup of the fibers on the fracture surface, and image c) 

shows a GnP aggregate lying between fibers, as highlighted by a white box.
157

Resin-rich regions of the 0.25 wt% GnP fracture surfaces are shown in Fig. 9.4.6. 

A GnP aggregate is highlighted in Fig. 9.4.6 by a green box. Submicron-scale 

voids are also present and have the characteristic shape, size, and texture of those 

formed by cavitation because of the presence of a phase-separated rubber 

toughening agent, as described previously.

 a) b)

Fig. 9.4.6 Intertow region of the 0.25 wt% GnP nanocomposite: a) low and b) high 

magnification

9.4.5.2 Low-Velocity Drop Weight Impact Properties

The impact test results for pristine, 0.25 wt%, and 1.0 wt% GnP specimens are 

presented in Figs. 9.4.7–9.4.11, and the corresponding peak loads are shown in

Table 9.4.1. The force-time histories of pristine, 0.25 wt%, and 1.0 wt% GnP 

specimens impacted at 20, 40, 60, and 80 J, respectively, are shown in Fig. 9.4.7.

Table 9.4.1 Impact results summary

Impact Energy

As Received 0.25wt% xGnPs 1wt% xGnPs

Peak 

Load

(kN)

Absorbed

Energy 

(J)

Peak 

Load

(kN)

Absorbed

Energy 

(J)

Peak 

Load

(kN)

Absorbed

Energy

(J)

20 7.661 4.744 7.483 6.155 6.945 11.216

40 11.399 16.614 11.497 17.868 9.33 26.724

60 12.974 48.326 12.129 43.385 11.663 44.456

80 13.855 a 14.478 a 11.672 75.59 a Perforation
158

0

2

4

6

8

10

12

14

16

0 2 4 6 8 10 12

Force, KN

Time, ms

20J

Pristine

0.25 wt% GnP

1.0 wt% GnP

0

2

4

6

8

10

12

14

16

0 2 4 6 8 10 12

Force, KN

Time, ms

40J

Pristine

0.25 wt% GnP

1.0 wt% GnP

0

2

4

6

8

10

12

14

16

0 2 4 6 8 10 12

Force, KN

Time, ms

60J

Pristine

0.25 wt% GnP

1.0 wt% GnP

0

2

4

6

8

10

12

14

16

0 2 4 6 8 10 12

Force, KN

Time, ms

80J

Pristine

0.25 wt% GnP

1.0 wt% GnP

Fig. 9.4.7 Force-time response of pristine, 0.25 wt%, and 1.0 wt% GnP specimens at 20-,

40-, 60-, and 80-J impact energies, respectively

At 20 J of impact energy, the specimen responses are symmetric about the peak 

load for both pristine and GnP-reinforced composite laminate plates, which 

indicates a lack of prominent specimen damage. Peak loads for the pristine 

specimens are larger than those reported in the literature for the S-2 Glass/SC-15 

system [26]. However, the peak load decreased with increasing GnP 

concentration, which could result from minor interlaminar damage and/or 

debonding between the polymer matrix and GnP nanoparticles. At 40-J impacts, 

the specimen responses exhibited a slight asymmetry about the peak load, 

indicating the presence of damage in the specimens. Also, at 40 J, the 1.0 wt% 

specimens exhibited a sharp load drop at about 7.8 kN, followed by a climb up to 

peak load. This load drop at 7.8 kN was also present for the 1.0 wt% samples at 

60 and 80 J but was not visible in the 1.0 wt% specimens tested at 20 J. This 

abrupt load drop may have resulted from interfacial failure in the specimens. 

At 60 and 80 J, the load-time specimen responses were substantially asymmetric 

about the peak load. Beyond this load point, several subsequent load drops 

occurred prior to the saturation level; the number of load drops was found to 

increase with increasing energy levels. Each load drop indicates some damage, 

which may be due to fiber breakage on the front and back surfaces and/or 

interfacial delamination. For both 60- and 80-J energy levels, the area under the 

load-time curves up to saturation was increased with increasing weight percent of 

GnPs, which implies that energy absorption performance increases with increasing 

addition of GnPs.
159

The energy-time histories of pristine, 0.25 wt%, and 1.0 wt% GnP specimens 

impacted at 20, 40, 60, and 80 J, respectively, are shown in Fig. 9.4.8, and the 

corresponding absorbed energies are noted in Table 9.4.1. 

0

5

10

15

20

25

30

0 2 4 6 8 10 12

Energy, J

Time, ms

20J Pristine

0.25 wt% GnP

1.0 wt% GnP

0

10

20

30

40

50

0 2 4 6 8 10 12

Energy, J

Time, ms

40J

Pristine

0.25 wt% GnP

1.0 wt% GnP

0

10

20

30

40

50

60

70

0 2 4 6 8 10 12

Energy, J

Time, ms

60J

Pristine

0.25 wt% GnP

1.0 wt% GnP

0

10

20

30

40

50

60

70

80

90

0 2 4 6 8 10 12

Energy, J

Time, ms

80J

Pristine

0.25 wt% GnP

1.0 wt% GnP

Rebounding energy

Impact Energy

Absorbed energy

E

t

Fig. 9.4.8 Energy-time response of pristine, 0.25 wt%, and 1.0 wt% GnP specimens at 20-, 

40-, 60-, and 80-J impact energies, respectively

At 20- and 40-J impact energies, energy absorption performance increased with 

increasing concentration of GnPs, where absorbed energy is defined as the 

difference between the impact and rebound energies and is shown schematically 

in Fig. 9.4.8. However, at 60 J, the energy absorption performance was highest for 

the pristine specimen; the GnP-reinforced samples exhibited the same trend as 

that described for 20- and 40-J impact energies. This anomaly may be the result of 

thickness variation between tested specimens. At 80 J, the energy absorption 

performance reached saturation for both pristine and 0.25 wt% GnP samples; 

perforation of the tested specimens by the impactor tup was observed. Contrarily, 

the 1.0 wt% GnP samples were able to absorb some of the impact energy, 

protecting the specimen from perforation.

The force-deflection histories of pristine, 0.25 wt%, and 1.0 wt% GnP specimens 

impacted at 20, 40, 60, and 80 J respectively, are shown in Fig. 9.4.9.
160

0

2

4

6

8

10

0 2 4 6 8 10

Force, KN

Deflection, mm

20J

Pristine

0.25 wt% GnP

1.0 wt% GnP

0

2

4

6

8

10

12

14

0 2 4 6 8 10

Force, KN

Deflection, mm

40J

Pristine

0.25 wt% GnP

1.0 wt% GnP

0

2

4

6

8

10

12

14

16

0 2 4 6 8 10 12 14 16

Force, KN

Deflection, mm

60J

Pristine

0.25 wt% GnP

1.0 wt% GnP

0

2

4

6

8

10

12

14

16

0 2 4 6 8 10 12 14 16

Force, KN

Deflection, mm

80J

Pristine

0.25 wt% GnP

1 wt% GnP

Fig. 9.4.9 Force-deflection response of pristine, 0.25 wt%, and 1.0 wt% GnP specimens at 

20, 40, 60, and 80 J impact energies, respectively

At 20, 40, and 60 J of impact energy, the pristine, 0.25 wt% GnP, and 1.0 wt% 

GnP specimens show a closed loop. This area inside the loop is the energy 

absorbed during impact, and the area under the curve is the energy transferred 

from the tup to the plate and back [27]. As expected, these loops were observed to 

increase with increasing impact energy. It is evident that none of the tested 

specimens were perforated within 60 J of impact energy. As described previously, 

at 80 J the pristine and 0.25 wt% GnP specimens were completely perforated. 

However, at 80 J the 1.0 wt% GnP specimens show a closed loop.

Analysis of Figs. 9.4.7–9.4.9 shows that the slopes of the pristine and 0.25 wt% 

GnP specimens are quite similar at all of the energies tested. However, the 

1.0 wt% GnP specimens have a different slope than the other concentrations 

tested in this study; this effect is more pronounced at higher impact energies. As 

such, it can be concluded that samples with 1.0 wt% interlaminar concentrations 

of GnP will exhibit different failure modes under low-velocity impact than the 

baseline or 0.25 wt% GnP specimens. 

Postimpact testing specimens were examined under DPI. Figure 9.4.10 shows the 

DPI images of pristine, 0.25 wt%, and 1.0 wt% GnP samples tested at 20, 40, 60,

and 80 J, respectively. 
161

20J 40J 60J 80J

Pristine

0.25 wt% 

GnP

1.0 wt% 

GnP

a) Front surface

20J 40J 60J 80J

Pristine

0.25 wt% 

GnP

1.0 wt% 

GnP

b) Back surface

Fig. 9.4.10 Dye-penetration inspection (DPI) images of impacted a) and back b) surfaces

The DPI images of the pristine specimens indicate prominent surface damage, 

which could have resulted from matrix and/or fiber breakage. This damage 

increases with increasing impact energy levels. At each energy level, the images 

show a decrease in damage area with increasing GnP concentration (down a 
162

column). At 80 J, both the pristine and 0.25 wt% GnP samples show evidence of 

perforation. However, at 1.0 wt% loadings, the damage appears to delocalize 

throughout the surface layers. The back sides of the impacted specimens were 

also analyzed via DPI and are shown in Fig. 9.4.10b. These images indicate that 

fiber breakage occurred for pristine and 0.25 wt% samples tested at 40, 60, and 

80 J. For 1.0 wt% samples, fiber breakage was only observed after an 80-J impact. 

It was also observed that the back surfaces have a larger damage area than the 

front surfaces, which was due to bending of the specimens by the impactor tup. 

The attenuation images from ultrasonic c-scanning of 0, 0.25, and 1.0 wt% GnPreinforced specimens impacted at 20, 40, 60, and 80 J, respectively, are shown in

Fig. 9.4.11. It was difficult to differentiate the defective and structurally sound 

areas using the attenuation images from the front surfaces facing the transducer. 

Therefore, only those images of the back surfaces are shown. 

Pristine 0.25 wt% GnP 1.0 wt% GnP

20J

40J

60J

80J

Fig. 9.4.11 C-scan images of back surfaces
163

The attenuation images indicate the extent of damage in the impact-tested 

specimens. White, closed loop-lines are drawn onto Fig. 9.4.11 to surround these 

areas. Within these loops, the white color indicates perforation and/or matrix and 

fiber breakage, while the deep blue color represents interfacial damage. Complete 

perforation was only observed for pristine and 0.25 wt% GnP specimens at 80 J. 

As such, the white regions in the other specimens represent significant surface 

damage. 

The damage profile of the pristine specimens is highly concentrated; a similar 

result is observed for the 0.25 wt% GnP specimens. However, at higher 

concentrations the GnPs are better able to absorb and dissipate impact energy 

throughout the specimens, leading to increased interfacial damage on the back 

surfaces. These trends were not observed at or near the impact surfaces.

9.4.6 Conclusion

We have developed a novel method for dispersing GnPs into the interply regions 

of FREPCs produced with VARTM. The concentration of GnPs in the 

nanocomposite was optimized to 0.25 wt%, providing a 29% improvement in 

flexural strength and a 25% improvement in mode-I fracture toughness. However, 

at higher loadings of GnPs, flexural strength parallels that of the baseline samples,

and drastic reductions in fracture toughness were observed. Fractographic 

investigation of the mode-I-tested specimens indicated that channel regions, 

where the warp and fill cross over, show a rougher fracture surface morphology 

by the addition of GnPs. Low-velocity drop-weight impact testing of FREPCs 

revealed enhanced energy absorption performance by interlaminar incorporation 

of GnPs. This enhancement was observed to increase with increasing 

concentration. Postimpact damage analysis by DPI and ultrasonic c-scanning 

qualitatively confirmed these results.

In conclusion, the relatively simple nanocomposite production method developed 

here is compatible with existing VARTM composite processing technologies and 

leads to significant improvements in both flexural strength and mode-I fracture 

toughness of FREPCs with continuous enhancement in impact damage resistance. 

Future work should be done to explore chemical functionalization of the 

nanoadditive to improve dispersion quality and interfacial interaction between the 

polymer matrix, fiber reinforcement, and GnPs, which may further increase both 

nanocomposite strength and toughness.
164

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