April 2014 

NASA/TM–2014-218245 

Additive Manufacturing Modeling and 

Simulation

 A Literature Review for Electron Beam Free Form Fabrication

 William J. Seufzer 

 Langley Research Center, Hampton, Virginia 
NASA STI Program . . . in Profile

Since its founding, NASA has been dedicated to the 

advancement of aeronautics and space science. The 

NASA scientific and technical information (STI) 

program plays a key part in helping NASA maintain 

this important role. 

The NASA STI program operates under the 

auspices of the Agency Chief Information Officer. 

It collects, organizes, provides for archiving, and 

disseminates NASA’s STI. The NASA STI 

program provides access to the NASA Aeronautics 

and Space Database and its public interface, the 

NASA Technical Report Server, thus providing one 

of the largest collections of aeronautical and space 

science STI in the world. Results are published in 

both non-NASA channels and by NASA in the 

NASA STI Report Series, which includes the 

following report types: 

• TECHNICAL PUBLICATION. Reports of 

completed research or a major significant phase 

of research that present the results of NASA 

Programs and include extensive data or 

theoretical analysis. Includes compilations of 

significant scientific and technical data and 

information deemed to be of continuing 

reference value. NASA counterpart of peerreviewed formal professional papers, but 

having less stringent limitations on manuscript 

length and extent of graphic presentations. 

• TECHNICAL MEMORANDUM. Scientific 

and technical findings that are preliminary or of 

specialized interest, e.g., quick release reports, 

working papers, and bibliographies that contain 

minimal annotation. Does not contain extensive 

analysis. 

• CONTRACTOR REPORT. Scientific and 

technical findings by NASA-sponsored 

contractors and grantees. 

• CONFERENCE PUBLICATION. 

Collected papers from scientific and 

technical conferences, symposia, seminars, 

or other meetings sponsored or cosponsored by NASA. 

• SPECIAL PUBLICATION. Scientific, 

technical, or historical information from 

NASA programs, projects, and missions, 

often concerned with subjects having 

substantial public interest. 

• TECHNICAL TRANSLATION. 

English-language translations of foreign 

scientific and technical material pertinent to 

NASA’s mission. 

Specialized services also include organizing 

and publishing research results, distributing 

specialized research announcements and feeds, 

providing information desk and personal search 

support, and enabling data exchange services. 

For more information about the NASA STI 

program, see the following: 

• Access the NASA STI program home page 

at http://www.sti.nasa.gov

• E-mail your question to help@sti.nasa.gov

• Fax your question to the NASA STI 

Information Desk at 443-757-5803 

• Phone the NASA STI Information Desk at 

443-757-5802 

• Write to: 

 STI Information Desk 

 NASA Center for AeroSpace Information 

 7115 Standard Drive 

 Hanover, MD 21076-1320
National Aeronautics and 

Space Administration 

Langley Research Center 

Hampton, Virginia 23681-2199 

 

April 2014 

NASA/TM–2014-218245 

Additive Manufacturing Modeling and 

Simulation

 A Literature Review for Electron Beam Free Form Fabrication

 William J. Seufzer 

 Langley Research Center, Hampton, Virginia 
Available from: 

NASA Center for AeroSpace Information 

7115 Standard Drive 

Hanover, MD 21076-1320 

443-757-5802 

Acknowledgments 

The author wishes to thank Karen Taminger and Robert Hafley for commissioning this 

literature review. The process of compiling this report has been a valuable learning 

exercise. 

The use of trademarks or names of manufacturers in this report is for accurate re porting and does not constitute an 

official endorsement, either expressed or implied, of such products or manufacturers by the National Aeronautics 

and Space Administration.
iii

Abstract 

Additive manufacturing is coming into industrial use and has several desirable attributes. Control 

of the deposition remains a complex challenge, and so this literature review was initiated to 

capture current modeling efforts in the field of additive manufacturing. This paper summarizes 

about 10 years of modeling and simulation related to both welding and additive manufacturing. 

The goals were to learn who is doing what in modeling and simulation, to summarize various 

approaches taken to create models, and to identify research gaps. Later sections in the report 

summarize implications for closed-loop-control of the process, implications for local research 

efforts, and implications for local modeling efforts.

 
iv

THIS PAGE INTENTIONALLY LEFT BLANK 
v

TABLE OF CONTENTS 

SECTION 1 OVERVIEW .............................................................................................. 1

SECTION 2 ENERGY INSERTION .............................................................................. 2

SECTION 3 MOLTEN POOL ....................................................................................... 4

3.1 Molten Pool Physics Based Models .................................................................... 4

3.2 Molten Pool Process Maps ................................................................................... 5

SECTION 4 MICROSTRUCTURE EVOLUTION .......................................................... 7

SECTION 5 RESIDUAL STRESS ................................................................................ 9

SECTION 6 MECHANICAL PROPERTY PREDICTION ............................................ 10

SECTION 7 DEPOSITION MODELING ..................................................................... 12

SECTION 8 DEPOSITION CONTROL ....................................................................... 14

SECTION 9 EVALUATION OF THE LITERATURE POOL ....................................... 16

9.1 Implications for CLC ........................................................................................... 16

9.2 Implications for Process Related Research ...................................................... 16

9.3 Implications for Modeling Efforts ...................................................................... 17

SECTION 10 REFERENCES ....................................................................................... 18
vi

 LIST OF TABLES 

Table 1. Faraday cup and beam energy distribution. ............................................. 3

Table 2. Molten pool physics. ................................................................................... 5

Table 3. Molten pool process maps. ........................................................................ 6

Table 4. Microstructure evolution. ........................................................................... 8

Table 5. Residual stress. ........................................................................................ 10

Table 6. Mechanical property prediction. .............................................................. 10

Table 7. Deposition modeling. ................................................................................ 13

Table 8. Deposition control. ................................................................................... 15
vii

Nomenclature 

2–D two–dimensional 

3–D three–dimensional 

AM additive manufacturing 

ANN artificial neural network 

ASME American Society of Mechanical Engineers 

CA cellular automata 

CLC closed loop control 

DOE design of experiments 

EB electron beam 

EBF3

 electron beam free form fabrication 

FE finite element (model) 

FV finite volume (model) 

GA genetic algorithm 

GGF greedy geometric feedback 

GTAW gas tungsten arc welding 

ICME integrated computation materials engineering 

ILC iterative learning control 

LB lattice Boltzmann 

LENS laser engineered net shaping 

M&S modeling and simulation 

PSO particle swarm optimization 

SFF solid freeform fabrication 
1 

 SECTION 1 

 OVERVIEW 

Additive manufacturing (AM) is coming into industrial use and has several desirable attributes. 

For example, it may be possible to control microstructural features through variations in additive 

processing parameters (ref. 1). If microstructure can be controlled then AM could contribute to a 

goal of the integrated computational materials engineering (ICME) community. ICME has a goal 

to deliver designer materials based on performance requirements (ref. 2). In addition to 

providing designer materials, additive manufacturing will also enable unique part design by 

removing some manufacturing process constraints (ref. 3). While other challenges such as 

certification must be overcome for everyday use, this paper will focus on what is currently being 

done to understand the deposition process through modeling and simulation (M&S). The 

published literature on laser-based additive manufacturing appears more extensive than electron 

beam-based wire deposition. Gu et al. provide an overview of laser-based powder additive 

manufacturing methods and review physical aspects and microstructural and mechanical 

properties (ref. 4). 

Through the literature search, this paper will: 

• identify key player in the AM modeling field 

• summarize approaches being taken to model and/or simulate the deposition process 

• pinpoint research gaps in M&S for AM 

Modeling and simulation of AM enables the design and implementation of process control 

methods. Therefore, paper selection also includes efforts on AM process control to provide 

insight into what other research or M&S effort is needed to improve process control. 

Further, the intent was to limit the literature review to AM papers and limit how much welding 

literature was reviewed. AM can be thought of as the consecutive placement of weld beads on 

top of previous beads to build a component layer by layer. As such, AM and welding share 

common physics related attributes. The welding literature contains a wealth of information on 

molten pool dynamics, energy insertion, microstructure development, and residual stress 

information that cannot be ignored and is thus included in this review. 

Topics are organized according to the flow of energy in the additive process: energy insertion, 

molten pool dynamics, microstructure evolution, residual stress and distortion, and material 

property prediction. Following that are summaries of literature that deal with deposition 

modeling and deposition control. The final section summarizes the implications this search may 

have on electron beam free form fabrication (EBF3

) efforts towards closed-loop-control (CLC), 

EBF3 process research, and EBF3 modeling efforts. 
2 

 SECTION 2 

ENERGY INSERTION 

Most of the literature in energy insertion has to do with laser energy sources. Observed 

phenomenon with laser and electron beam energy inputs include keyhole energy physics, 

vaporization losses, and energy loss to vapor clouds. 

T. Zhang, Zheng, and Zhao (2013) showed that using two different energy source models can 

result in two different thermal distributions in the substrate. One model of the energy beam more 

accurately describes the physics and timing of the real system (pulsed timing with a parabolic 

heat distribution) while the other model was typically used to model heat input in various studies 

(a static Gaussian distribution). Experiments were conducted to confirm that the pulsed model 

more accurately predicts thermal distribution. The energy source must be modeled accurately 

since thermal distributions determine residual stress fields and microstructure evolution, (ref. 5). 

Shen and Chou (2012) showed that larger electron beam diameters resulted in lower molten pool 

temperatures for the same energy input. This affects cooling rates and therefore the resulting 

microstructure (ref. 6). 

Peng et al. (2011) described a complete system for characterizing an electron beam, which 

included an equation for relating measured voltage to beam density. It also related the geometry 

of the beam distribution to accelerating voltage, beam current, travel speed, and focus. The 

three–dimensional (3–D) shape of the beam, if altered by faulty cathode geometry, could yield an 

asymmetric weld bead (ref. 7). 

A group of papers that describe Faraday cup design and application are summarized in 

chronological order to help show the progression through time. Elmer and Teruya (2001) 

described an enhanced Faraday cup design (ref. 8). Palmer and Elmer (2007a) showed a nearly 

20 percent difference in two electron beam welders with similar beam parameter settings (ref. 9). 

Palmer et al. (2007b) described a procedure for transferring electron beam welding parameters 

between different machines which produced similar welds on both machines with small error 

after calibration (ref. 10). Palmer (2008) tracked 90 welds over 18 months while beam parameter 

variations were controlled to within ±2.2 percent. The study cited that this variation easily fell 

withn the 5 percent tolerance specified by the American Society of Mechanical Engineers’ 

(ASME). This study also included detailed data showing variation in operator determined sharpfocus settings (ref. 11). 

Safdar, Li, and Sheikh (2007) studied laser beam geometry and energy density and distribution 

from four different beam geometries. The variations were shown to affect temperature 

distribution, heating/cooling rates, and fluid flow. A series of finite volume simulations agreed 

with experimental results. The simulations revealed beam geometry effects related to 

conductivity and Marangoni flow (ref. 12). 

Nath et al. (2002) presented a laser welding study that showed how coupling efficiency changes 

with energy transfer mode in austenitic stainless steel. In conduction mode 15 percent of the 

lasers energy was transferred into the material. In keyhole mode 65 percent of the lasers energy 
3 

was transferred. This paper also cites changes in microstructure based on travel speed with 

higher travel speed resulting in solidification cracking (ref. 13). 

Wei and Chow’s (1992) research reported variations in fusion zone geometries with different 

focal locations, spot sizes, and convergence angles in electron beam processing. The study 

described a 3–D steady-state model of the energy beam, molten pool cavity, and energy 

absorption. The model was supported by experimental data showing that energy flux is 

governed by focal spot location, energy distribution at the focal spot, and convergence angle (ref. 

14). 

Table 1. Faraday cup and beam energy distribution. 

Year First 

Author Ref. Title Affiliation 

2013 Zhang [5] A dynamic welding heat source model in pulsed current 

gas tungsten arc welding. China 

2012 Shen [6] Thermal modeling of electron beam additive 

manufacturing process – powder sintering effects. USA 

2011 Peng [7] Beam quality test technology and devices of electron 

beam welding. China 

2008 Palmer [11] Improving process control in electron beam welding 

using the enhanced modified Faraday cup. USA 

2007 Safdar [12] 

Numerical analysis of the effects of non-conventional 

laser beam geometries during laser melting of metallic 

materials. 

UK 

2007b Palmer [10] Transferring electron beam welding parameters using 

the enhanced modified Faraday cup. USA 

2007a Palmer [9] 

Characterization of electron beams at different focus 

settings and work distances in multiple welders using 

the enhanced modified Faraday cup. 

USA 

2002 Nath [13] Laser power coupling efficiency in conduction and 

keyhole welding of austenitic steel. India 

2001 Elmer [8] An enhanced Faraday cup for rapid determination of 

power density distribution in electron beams. USA 

1992 Wei [14] Beam focusing characteristics and alloying element 

effects on high-intensity electron beam welding. Taiwan 

 
4 

 SECTION 3 

MOLTEN POOL 

This section includes electron beam and laser welding papers. Other heat sources are considered 

only if there is something of relevance to the molten pool dynamics that might need to be 

considered. 

3.1 Molten Pool Physics Based Models 

The first of four papers by Wei, Liu, and Lin (2012a, ref. 15) thoroughly cover fundamental 

physical principles related to molten pool dynamics. All of these papers may offer insight to 

modeling molten characteristics such as temperature distribution and geometry without 3–D 

finite element simulation. The authors made use of Prandtl and Marangoni numbers and scale 

analysis. See also Wei, Lin, Liu, and Ting (2012b, ref. 16), Wei, Lin, Liu, and DebRoy (2012c, 

ref. 17), and Wei and Liu (2012d, ref. 18). 

Fan and Liou (2012) provided a technical overview of the equations needed to model the physics 

of a molten pool. The two–dimensional (2–D) simulation, parameterized for Ti–6Al–4V, was 

able to show free surface movement based on surface tension and wetting forces (ref. 19). 

Daneshkah, Najafi, and Torabian’s (2012) study was based on laser keyhole welding and shows 

equations for modeling a 3–D volumetric heat source. The introduction section summarized 

other keyhole model heat source techniques that might be of interest. The described 3–D model 

was validated and agreed well with experimental data (ref. 20). 

W. Zhang et al. (2012) conducted a welding study where backside bead width was predicted 

from molten pool width, length, and convexity in gas tungsten arc welding (GTAW). Five 

parameters were considered, but these three were shown to be the most effective in a least 

squares algorithm. The variance of the optimal model was small compared to the precision 

required for a feedback control system (ref. 21). 

Manvatkar et al. (2011) used Abaqus (formerly ABAQUS) to create a 3–D heat flow model of 

SS 316 laser engineered net shaping (LENSTM) deposition. A custom subroutine used 0.20 mm 

cube volumes to estimate molten pool growth and geometry as powder was added. The geometry 

was not predicted by using the physics relationships of surface tension and wetting forces. 

Computed temperature profiles were used to estimate yield strength with the Hall–Petch 

coefficients (ref. 22). 

Rai, Palmer, et al. (2009a) described a numerical 3–D heat transfer model for an electron beam 

welder in keyhole mode. The model computed molten pool fluid flow and its effect on weld bead 

geometry. The paper covered experimental verification and provideed many references on model 

creation (ref. 23). 

Rai, Burgardt, et al. (2009b) calculated the keyhole shape in electron beam welding based on an 

energy balance model. Molten pool fluid flow and heat transfer were computed for keyhole 

welding (ref. 24). 
5 

Rai, Kelly, et al. (2008) described calculations of keyhole geometry based on material properties 

and a 3–D finite element model for heat flow (ref. 25). 

Rai, Roy, and DebRoy (2007) described a model similar to Rai, Kelly, et al. (2008) but 

compared the different thermal conductivities of SS 304L and Al 5754. The model predicted 

different molten pool geometries of the high and low Peclet number systems. The Peclet number 

is the ratio of convective to conductive heat transfer (ref. 26). 

Table 2. Molten pool physics. 

Year First 

Author Ref. Title Affiliation 

2012a Wei [15] Scaling weld or melt pool shape induced by 

thermocapillary convection. Taiwan 

2012b Wei [16] Transient thermocapillary convection in a molten or 

weld pool. Taiwan 

2012c Wei [17] 

Scaling weld or melt pool shape affected by 

thermocapillary convection with high Prandtl 

numbers. 

Taiwan 

2012d Wei [18] Scaling thermocapillary weld pool shape and 

transport variables in metals. Taiwan 

2012 Fan [19] Numerical modeling of the additive manufacturing 

(AM) processes of titanium alloy. USA 

2012 Daneshkhah [20] Numerical simulation of weld pool shape during 

laser beam welding. Iran 

2012 Zhang [21] Characterization of three-dimensional weld pool 

surface in GTAW. USA 

2011 Manvatkar [22] 

Estimation of melt pool dimensions, thermal cycle, 

and hardness distribution in the laser-engineered 

net shaping process of austenitic stainless steel. 

India 

2009a Rai [23] Heat transfer and fluid flow during electron beam 

welding of 304L stainless steel alloy. USA 

2009b Rai [24] Heat transfer and fluid flow during electron beam 

welding of 21Cr-6Ni-9Mn steel and Ti-6Al-4V alloy. USA 

2008 Rai [25] 

A convective heat-transfer model for partial and full 

penetration keyhole mode laser welding of a 

structural steel. 

USA 

2007 Rai [26] 

A computationally efficient model of convective 

heat transfer and solidification characteristics 

during keyhole mode laser welding. 

USA 

 

3.2 Molten Pool Process Maps 

Vasinonta, Beuth, and Griffith (2007) constructed process maps to predict molten pool geometry 

for thin-walled laser powder AM structures (ref. 27). The findings assert that “melt pool length 
6 

was a strong function of laser power and velocity, and was a weak function of preheat 

temperature” (ref. 27, p. 107). 

Vasinonta, Beuth, and Griffith (2001a) showed process map models that assume temperature 

independent material properties. This paper showed details about process map development, 

experimental verification, and example calculations (ref. 28). 

Vasinonta, Beuth, and Ong (2001b) used process maps for molten pool geometry prediction and 

compared results from thin-walled and bulk deposits. Results showed that more heat input is 

needed for the bulk deposits to maintain a constant molten pool size (ref. 29). 

Vasinonta,Beuthm and Griffith (2000) laid the groundwork for process map research that was 

used in subsequent papers detailed above (ref. 30). 

Table 3. Molten pool process maps. 

Year First 

Author Ref. Title Affiliation 

2007 Vasinonta [27] 

Process maps for predicting residual stress and 

melt pool size in the laser-based fabrication of 

thin-walled structures. 

USA 

2001a Vasinonta [28] 

A process map for consistent build conditions in 

the solid freeform fabrication of thin-walled 

structures. 

USA 

2001b Vasinonta [29] Melt pool size control in thin-walled and bulky 

parts via process maps. USA 

2000 Vasinonta [30] Process maps for controlling residual stress and 

melt pool size in laser-based SFF processes. USA 

 
7 

 SECTION 4 

MICROSTRUCTURE EVOLUTION 

This section on microstructure evolution and the next on residual stress are somewhat related. 

Papers in both sections relate the process to the microstructure. Due to the volume of papers that 

focus on residual stress, that topic is given its own section. 

Jingwei Zhang et al. (2013) used a 2–D cellular automata (CA) approach for simulating 

microstructure evolution in SS 316. This model will eventually be expanded to 3–D and 

validated with experimental data from a laser/powder based AM system. (ref. 31). 

Eshraghi, Felicelli, and Jelinek (2012a) used combined lattice Boltzmann (LB) and CA methods 

to simulate solute-driven dendrite growth. This combination is easily parallelized and exhibits 

good parallel scalability compared to finite element and finite volume approaches. These 

attributes are necessary for increasing the scale of computation to macroscopic levels and, while 

adding the ability to handle several types of grain evolution, include fluid flow and temperature 

calculations (ref. 32). Eshraghi and Felicelli (2012b) revealed a lattice Boltzmann model for heat 

conduction and phase change (ref. 33). 

Barrales-Mora, Gottstein, and Shvindleman (2012) simulated “the effect of finite triple junction 

mobility on the growth rate of 2–D polycrystals” (ref. 34, p. 546). 

Amoorezaei, Gurerrich, and Provatas (2012) demonstrated “computationally and experimentally 

that a material’s surface tension anisotropy can compete with anisotropies present in processing 

conditions during solidification...” (ref. 35, p.657). 

Brandl et al. (2011) showed microstructural characteristics of Ti–6Al–4V single bead deposits in 

relation to deposition parameters. The article is based on laser wire deposition and includes 

tables of data, micrographs, and an extensive bibliography. This paper does not contain any 

discussion in regard to modeling but provides a fair amount of experimental data and discussion 

of interest to a deposition modeler (ref. 36). 

Groeber (2010) presented two approaches for representing microstructure, explicit and statistical. 

Developing an accurate and sufficient representation microstructure is necessary for 

computational studies that hope to compute material properties from microstructure (ref. 37). 

Barrales-Mora, Gottstein, and Shvindleman (2008) simulated normal grain growth from single 

grains to polycrystals. The simulation used a 3–D vertex model that agreed well with various 

analytical approaches. Simulation results were compared to previous models (ref. 38). 

Rai, Kelly, et al. (2008) presented a 3–D laser keyhole model parameterized with A131 structural 

steel properties for experimental verification. “The solidification microstructure tends to become 

more dendritic with increase in laser power, and coarser with increase in heat input per unit 

length. The microstructure also varies with location due to spatial variation of the cooling rate” 

(ref. 25, p. 107). See also Rai, Roy, and DebRoy (2007, ref. 26). 

Bontha et al. (2006) used a 2–D Rosenthal solution for a moving heat source to develop process 

maps for predicting microstructure. Process parameters for LENSTM Ti–6Al–4V deposition on a 
8 

thin wall are mapped to microstructure. This work shows that laser power and velocity change 

cooling rates by several orders of magnitude (ref. 39). 

Demirel et al. (2003) simulated evolved microstructure at the scale of individual grains in 

aluminum foil. In the study, computed microstructure evolution using curvature-driven grain 

boundary motion and anisotropic interface properties showed better agreement with experimental 

results than when isotropic properties are used (ref. 40). 

Yang et al. (2000) showed a 3–D monte carlo simulation of grain growth in a titanium weld bead 

that demonstrated good agreement with experimental data. The paper noted that earlier 2–D 

simulations did not show good agreement with experimental data. This suggests that grain 

growth prediction must consider all three dimensions (ref. 41). 

Table 4. Microstructure evolution. 

Year First 

Author Ref. Title Affiliation 

2013 Zhang [31] Probabilistic simulation of solidification microstructure evolution during laser-based metal deposition. USA 

2012a Eshraghi [32] 

Three dimensional simulation of solutal dendrite 

growth using lattice Boltzmann and cellular 

automata methods. 

USA 

2012b Eshraghi [33] An implicit lattice Boltzmann model for heat 

conduction with phase change. USA 

2012 BarralesMora [34] 

Effect of a finite boundary junction mobility on the 

growth rate of grains in two-dimensional 

polycrystals. 

Germany 

2012 Amoorezaei [35] Orientation selection in solidification patterning. Canada 

2011 Brandl [36] Deposition of Ti-6Al-4V using laser and wire, part 1: 

Microstructural properties of single beads. Germany 

2010 Groeber [37] Digital representation of materials grain structure. USA 

2008 BarralesMora [38] Three-dimensional grain growth: Analytical 

approaches and computer simulations. 

Germany, 

Russia 

2008 Rai [25] 

A convective heat-transfer model for partial and full 

penetration keyhole mode laser welding of a 

structural steel. 

USA 

2007 Rai [26] 

A computationally efficient model of convective heat 

transfer and solidification characteristics during 

keyhole mode laser welding. 

USA 

2006 Bontha [39] 

Thermal process maps for predicting solidification 

microstructures in laser fabrication of thin-wall 

structures. 

USA 

2003 Demirel [40] Bridging simulations and experiments in 

microstructure evolution. USA 

2000 Yang [41] Three dimensional monte carlo simulation of grain 

growth during GTA welding of titanium. USA 
9 

 SECTION 5 

RESIDUAL STRESS 

Lindgren, Lundbåck, and Fisk (2013) stated that strong nonlinearities and large deformations are 

difficult to model. Residual stress and deformations are very dependent on material behavior. 

This is a very recent finite element (FE) model applied to manufacturing simulations (ref. 42). 

Ding et al. (2011) summarized a finite element model for an additive manufacturing process. A 

FE model is used as input to a 3–D mechanical model that computed residual stress and 

distortion. The model output was compared to experimental results and data from an ENGIN–X 

neutron diffraction strain scan (ref. 43). 

De and DebRoy (2011) presented an editorial on welding induced residual stress with an 

extensive bibliography of 120 papers related to residual stress and residual stress modeling (ref. 

44). 

Tian et al. (2008) simulated the temperature field and mechanical aspects in electron beam (EB) 

welding of a large (1 meter) aluminum part with 8 meters of weld. The simulation included a 3–

D model of keyhole physics. This was a systematic study of process parameters to minimize 

distortion to the welded structure. Pre-deformation affected the final distortion but was not as 

much of an influence as process parameters (ref. 45). 

Vasinonta, Beuth, and Griffith (2007) showed process maps for thermal gradients and that heat 

input and travel velocity selection can cause a 20 percent change in residual stress in SS304. 

“The biggest payoff in reducing residual stresses comes from uniform baseplate (and wall) 

preheating...” to 400 °C (ref. 27, p. 107). 

Vasinonta (2000) wrote, “The maximum reduction of residual stress by preheating the part is 

approximately 40 percent and is achieved by preheating the part to 400 °C” (ref.30, p. 207). This 

paper lays the groundwork for the later papers. 
10 

Table 5. Residual stress. 

Year First 

Author Ref Title Affiliation 

2013 Lindgren [42] Thermo-mechanics and microstructure evolution in 

manufacturing simulations. Sweden 

2011 Ding [43] 

Thermo-mechanical analysis of wire and arc additive 

layer manufacturing processes on large multi-layer 

parts. 

UK 

2011 De [44] A perspective on residual stresses in welding India, USA 

2008 Tian [45] Finite element modeling of electron beam welding of 

a large Al alloy structure by parallel computations. 

China, 

Canada 

2007 Vasinonta [27] 

Process maps for predicting residual stress and melt 

pool size in the laser-based fabrication of thin-walled 

structures. 

USA 

2000 Vasinonta [30] Process maps for controlling residual stress and melt 

pool size in laser-based SFF processes USA 

 SECTION 6 

MECHANICAL PROPERTY PREDICTION 

Roy et al. (2013) used a design of experiments (DOE) method to derive an empirical model 

relating welding process parameters to weld hardness, ultimate tensile load, and toughness. With 

the empirical model, a genetic algorithm was used to search for process parameters that meet 

desired combinations of mechanical properties (ref. 46). 

Manvatkar et al. (2011) discussed a 3–D heat flow model, built in Abaqus, for predicting 

hardness in LENSTM processing. This model overpredicted hardness because of the 

underprediction of cell spacing. This model used Hall–Petch coefficients that are normally used 

for wrought and annealed grain structures which may have also contributed to an overprediction 

of the hardness (ref. 22). 

Brandl et al. (2011) wrote part two of a two part series. The article showed that hardness 

measurements do not necessarily correlate with thermal history. There is some correlation 

between thermal history and bead dimensions. This paper does not contain any discussion 

concerning modeling but provides a fair amount of experimental data and discussion that might 

be considered by a deposition modeler (ref. 47). 

Robertson et al. (2011), while not specifically discussing mechanical property prediction, 

summarized the state of the art in material characterization technologies. This paper is of interest 

as simulation validation would rely on these technologies (ref. 48). 

Guo et al. (2009) showed recent developments in material properties modeling for castings. 

While not related directly to additive manufacturing, this paper may provide insight to the state 

of the art in materials property prediction in simulation (ref. 49). 
 11

Table 6. Mechanical property prediction. 

Year First 

Author Ref Title Affiliation 

2013 Roy [46] 

An approach for solving multi characteristics 

optimization of submerged arc welding parameters 

by using grey based genetic algorithm 

India 

2011 Manvatkar [22] 

Estimation of melt pool dimensions, thermal cycle, 

and hardness distribution in the laser-engineered net 

shaping process of austenitic stainless steel. 

India 

2011 Brandl [47] Deposition of Ti-6Al-4V using laser and wire, part II: 

Hardness and dimensions of single beads. Germany 

2011 Robertson [48] Towards an integrated materials characterization 

toolbox. USA 

2009 Guo [49] Materials properties for process simulation. UK 

 
12 

 SECTION 7 

DEPOSITION MODELING 

Tong et al. (2013) described an interesting approach to multiscale, multiphysics modeling. This 

paper summarizes an approach to fusion welding that considers all scales from molecular 

dynamics up through to the macro-scale thermal field. To accomplish this, the authors did not 

attempt to mix all scales into one large model as is typically done. Instead, each model was 

designed to work on its own with its own appropriately scaled mesh and solver. An upper level 

algorithm moved data from model-to-model as they step through time. This was no small effort. 

This paper has 20 authors from 10 different locations including Ireland, the UK, Urbana IL, 

Switzerland, the Netherlands, and Germany (ref. 50). 

Lundbåck and Lindgren (2011) discussed a complete finite element model of multipass welding. 

The model was validated against experimental results to include thermal history and distortions 

due to residual stress. Thermal history data from the model showed good agreement with 

experimental data. Deformation calculations also showed good agreement. The model was 

generic in the sense that material is added by activating mesh elements. There was no explicit 

model of added wire or powder. The bibliography appears to capture previous work done in this 

type of FE-related AM modeling (ref. 51). 

Fallah et al. (2011) gave an FE approach to laser powder deposition. This study modeled the 

real-time molten pool shape to predict molten pool and solidification geometries. This was done 

without a multiphysics-based model of the molten pool. Instead, a fine mesh of elements was 

activated based on a set of mass flow equations. The geometry prediction appears to be 

somewhat predetermined but has a higher resolution than just adding large rectangular elements 

as the energy beam passes (ref. 52). 

Bag and De (2010) and Bag, De, and DebRoy (2009) showed a FE 3–D heat transfer and fluid 

flow model that was used to optimize GTAW welds. There are four parameters in the model that 

were uncertain: coupling efficiency, effective arc radius, effective thermal conductivity, and 

viscosity. To find values for these coefficients the model was integrated with a Genetic 

Algorithm (GA) that finds optimal values for these parameters by matching simulation output 

with experimental samples. Once the values were computed they were then used to parameterize 

welds with various requirements for final weld geometry (ref. 53 and 54). 
13 

Table 7. Deposition modeling. 

Year First 

Author Ref Title Affiliation 

2013 Tong [50] 

Multiscale, multiphysics numerical modeling of fusion 

welding with experimental characterization and 

validation. 

USA, 

European 

2011 Lundbåck [51] Modeling of metal deposition. Sweden 

2011 Fallah [52] Temporal development of melt-pool morphology and 

clad geometry in laser powder deposition. Canada 

2010 Bag [53 

Probing reliability of transport phenomena based heat 

transfer and fluid flow analysis in autogeneous fusion 

welding process. 

India 

2009 Bag [54] A genetic algorithm assisted inverse convective heat 

transfer model for tailoring weld geometry 

India, 

USA 
14 

 SECTION 8 

DEPOSITION CONTROL 

W. Zhang and Y. Zhang (2012) monitored the weld pool of a GTAW system and the responses 

of a novice human welder. The human welder was sufficiently trained such that responses were 

not random. Part 1 (ref. 55) discussed the experimental setup and data collection. Part II (ref. 56) 

discussed the results. A system identification method was used with weld pool geometry (length, 

width, and convexity) as the input variables. The model was able to show that a manual welder 

makes control responses based on observations made 1.5 to 3 seconds earlier. Adjustments to the 

weld current were also based on previous adjustments made 1 second earlier. There was no 

discussion of using the model as a control system sans human, but it would seem to be the next 

logical step. 

Heralić, Christiansson, and Lennartson (2012) demonstrated a machine learning algorithm 

applied to deposition control. While this paper was not about a numerical model derived for 

control, machine learning does imply some type of model that is able to predict or correct and, 

therefore, is included here. This system used a 3–D scanner to measure previous deposition 

heights and then controls wire feed rate, in feed forward fashion, to make height corrections in 

the next deposited layer (ref. 57). 

Tang and Landers (2011) showed height control for laser powder deposition based iterative 

learning control (ILC) and particle swarm optimization (PSO). To develop height control, a 

process model was built and tested. PSO was used to estimate model parameters from measured 

temperature and track height profiles. ILC was then used to schedule powder flow rates for the 

next layer to obtain the desired height profile (ref. 58). 

Cohen and Lipson (2011) proposed greedy geometric feedback (GGF) for closing the loop in 

controlling solid freeform fabrication (SFF). Typical CLC designs monitor and manipulate low 

level system parameters (feed rates and energy) but do not necessarily guarantee accurate final 

part geometry. The GGF method proposed by the authors manipulated deposition location (mass 

and location) to compensate for geometric inaccuracies. An SFF system was shown that 

implemented GGF, but results were only compared to open-loop deposition. GGF was better 

than open-loop, but no comparison was made to any other CLC design (ref. 59). 

Chandrasekhar and Vasudevan (2010) described a method to optimize activated flux tungsten 

inert gas welding (A–TIG) process parameters to achieve desired weld geometry. In this study a 

genetic algorithm (GA) was used to tune an artificial neural network (ANN) from experimental 

data. This study was included here to illustrate the amount of experimental data required by this 

type of approach for a limited operational range and for each working alloy. For each alloy, 120 

single bead deposits were made and 3 output parameters were measured; depth of penetration, 

bead height, and bead width (ref. 60). 
15 

Table 8. Deposition control. 

Year First 

Author Ref Title Affiliation 

2012a Zhang [55] Modeling of human welder response to 3−D 

weld pool surface: Part I—Principles. USA 

2012b Zhang [56] Modeling of human welder response to 3−D 

weld pool surface: Part II—Results and analysis. USA 

2012 Heralić [57] 

Height control of laser metal-wire deposition 

based on iterative learning control and 3−D 

scanning. 

Sweden 

2011 Tang [58] Layer-to-layer height control for laser metal 

deposition process. USA 

2011 Cohen [59] Geometric feedback control of discretedeposition SFF systems. USA 

2010 Chandrasekhar [60] Intelligent modeling for optimization of A-TIG 

welding process India 

 
16 

 SECTION 9 

EVALUATION OF THE LITERATURE POOL 

The goals of this literature survey were to document the state of the art in modeling and 

simulation as applied to additive manufacturing and determine who is doing the work. About 

half of the papers in this survey are from Asia, Canada, and Europe. Papers from China appear to 

be more focused on welding technology while those from India are focused on fundamental work 

in optimization and control. European, Canadian, and USA papers cover all aspects of the 

additive manufacturing process. 

9.1 Implications for CLC 

Molten pool temperature alone is not a good indicator for CLC; there are many molten pool 

geometries with similar temperatures (ref. 58). From local experience, geometry alone may not 

be a good indicator for fine CLC control. Perhaps a mix of both is needed. 

It is possible to maintain energy input while decreasing the temperature of the molten pool by 

increasing the area of the energy beam (ref. 6). There are several papers that suggest that beam 

shape, or energy distribution, should be considered in CLC design (refs. 5, 6, 7, 12, 13, 14). That 

is, the electron beam geometry should be held constant and calibrated with a Faraday cup or 

DOE experiments need to consider electron beam geometry. 

Empirical approaches have been taken to control deposition processes and have proven effective. 

Experimental data was collected and used to develop either a set of empirical equations or the 

data was used to train a machine learning method. These approaches are effective but require 

large amounts of data and careful analysis. They may be useful for a bounded set of deposition 

parameter settings but would require a significant amount of data to handle a multitude of alloy 

systems and gradient deposits. 

A great deal of effort has been spent on modeling fusion welding and deposition processes. For 

what has been learned, there has not been a breakthrough moment in terms of how to use what is 

known for effective control. Process control has been advanced through M&S for specific metals 

and specific process parameter regions of interest, but there is no unifying equation or other 

understanding that reveals mastery of the process. 

9.2 Implications for Process Related Research 

Repeatable builds with an electron beam may require a higher level of calibration and 

characterization of the heat source. Those in the field currently treat and model the energy source 

as a blunt and simple thing, which it is not. For modeling, is it possible to model the heat source 

and penetration and exploit energy insertion for better control of microstructure in parameter 

ranges that might otherwise be bad? Can bad parameter sets become favorable by changing the 

energy distribution? How might variations in focus and energy distribution at this level affect 

microstructure evolution? 

The shape of the energy beam is important. Faraday cup calibration should be used to ensure 

consistent beam quality during an individual experiment and across multiple experiments where 
17 

comparisons can be drawn. This will be especially important as two or more electron beam 

systems are used to produce consistent and repeatable samples. 

The current research literature appears limited to single factor experiments while the underlying 

physics are far more complicated. Multifactor experimental designs, such as DOE, would 

certainly enhance the literature pool. 

For industrial applications and research efforts, consideration must be made for calibrating two 

different energy sources that may be tasked with building the same part. Any differences 

between energy sources must be minimized through calibration or through parameter settings in 

a CLC. 

9.3 Implications for Modeling Efforts 

The shape of the energy beam is important. One cannot assume that a simple double elliptical 

beam model, which is typically used, is adequate. Shape can affect the thermal field which in 

turn affects the molten pool depth, undercooling rates, microstructure, residual stress, and 

distortion. 

The finite element and finite volume (FV) approaches have repeatedly been able to predict 

thermal fields and have been effective in predicting residual stress and distortion. Experiments 

comparing FE and FV modeling have consistently shown good agreement. However, care should 

be taken when choosing mesh density, solvers, and simulation time step size. While the FE and 

FV methods are effective, some level of validation should be exercised to increase confidence in 

what is being modeled. 

To effectively model across multiple scales, from molecular to macroscopic, it may be more 

efficient to build and operate models appropriate to each scale level and then integrate them 

through data sharing. This will become easier to accomplish as the cost of computing 

infrastructure continues to decrease. 
18 

 SECTION 10 

REFERENCES 

1. Murr, Lawrence E., Gaytan, Sara M., Ramirez, Diana A., Martinex, Edwin, Hernandez, 

Jennifer, Amato, Krista N., Shindo, Patrick W., Medina, Francisco R., and Wicker, Ryan 

B., "Metal Fabrication by Additive Manufacturing Using Laser and Electron Beam 

Melting Technologies," Journal of Materials Science & Technology, Vol. 28, No. 1, 

2012, pp. 1–14. 

2. Panchal, Jitesh H., Kalidindi, Surya R., and McDowell, David L., "Key Computational 

Modeling Issues in Integrated Computational Materials Engineering," Computer-Aided 

Design, Vol. 45, No. 1, 2013, pp. 4–25. 

3. Lipson, Hod, "Design in the Age of 3–D Printing," Mechanical Engineering, September 

2012, pp. B–G. 

4. Gu, D. D., Meiners, W., Wissenbach, K., and Poprawe, R., "Laser Additive 

Manufacturing of Metallic Components: Materials, Processes and Mechanisms," 

International Materials Reviews, Vol. 57, No. 3, 2012, pp. 133–164. 

5. Zhang, Tong, Zheng, Zhentai, and Zhao, Rui, "A Dynamic Welding Heat Source Model 

in Pulsed Current Gas Tungsten Arc Welding," Journal of Materials Processing 

Technology, Vol. 213, No. 12, December 2013, pp. 2329 –2338. 

6. Shen, Ninggang (George) and Chou, Kevin, "Themal Modeling of Electron Beam 

Additive Manufacturing Process—Powder Sindering Effects," Proc. the 7th ASME 2012 

International Manufacturing Science and Engineering Conference (MSEC2012), 2012, 

ASME. 

7. Peng, Y., Wang, K. H., Zhou, Q., Wang, Y. J., and Fu, P. F., "Beam Quality Test 

Technology and Devices of Electron Beam Welding," Vacuum, Vol. 86, No. 3, 2011, pp. 

261–266. 

8. Elmer, J. W. and Teruya, A. T., "An Enhanced Faraday Cup for Rapid Determination of 

Power Density Distribution in Electron Beams," Welding Journal, Vol. 80, No. 12, 2001, 

pp. 288S–295S. 

9. Palmer, T. A. and Elmer, J. W., "Characterisation of Electron Beams at Different Focus 

Settings and Work Distances in Multiple Welders Using the Enhanced Modified Faraday 

Cup," Science and Technology of Welding and Joining, Vol. 12, No. 2, 2007, pp. 161–

174. 

10. Palmer, T.A., Elmer, J. W., Nicklas, K. D. and Mustaleski, T., "Transferring Electron 

Beam Welding Parameters Using the Enhanced Modified Faraday Cup," Welding 

Journal, Vol. 86, No. 12, 2007, p. 388S–398S. 
19 

11. Palmer, T. A. and Elmer, J. W. "Improving Process Control in Electron Beam Welding 

Using the Enhanced Modified Faraday Cup," Journal of Manufacturing Science and 

Engineering-Transactions of the ASME, Vol. 130, No. 4, 10 July 2008. 

12. Safdar, S., Li, L., and Sheikh, M. A., "Numerical Analysis of the Effects of Nonconventional Laser Beam Geometries during Laser Melting of Metallic Materials," 

Journal of Physics D-Applied Physics, Vol. 40, No. 2, 2007, pp. 593–603. 

13. Nath, A. K., Sridhar, R., Ganesh, P., and Kaul, R., "Laser Power Coupling Efficiency in 

Conduction and Keyhole Welding of Austenitic Stainless Steel," Sadhana-Academy 

Proceedings in Engineering Sciences, Vol. 27, Part 3, June 2002, pp. 383–392. 

14. Wei, P. S. and Chow, Y. T., "Beam Focusing Characteristics and Alloying Element 

Effects on High-Intensity Electron-Beam Welding," Metallurgical Transactions BProcess Metallurgy, Vol. 23, No. 1, 1992, pp. 81–90. 

15. Wei, P. S., Liu, H. J., and Lin, C. L., "Scaling Weld or Melt Pool Shape Induced by 

Thermocapillary Convection," International Journal of Heat and Mass Transfer, Vol. 55, 

No. 9–10, 2012, pp. 2328–2337. 

16. Wei, P. S., Lin, C. L., Liu, H. J., and Ting, C. N., "Transient Thermocapillary Convection 

in a Molten or Weld Pool," Journal of Manufacturing Science and EngineeringTransactions of the ASME, Vol. 134, No. 1, 11 January 2012. 

17. Wei, P. S., Lin, C. L., Liu, H. J., and DebRoy, T., "Scaling Weld or Melt Pool Shape 

Affected by Thermocapillary Convection With High Prandtl Numbers," Journal of Heat 

Transfer-Transactions of the Asme, Vol. 134, No. 4, 16 February 2012. 

18. Wei, P. S. and Liu, H. J., "Scaling Thermocapillary Weld Pool Shape and Transport 

Variables in Metals," Welding Journal,Vol. 91, No. 7, 2012, pp. 187S–194S. 

19. Fan, Zhiqiang and Liou, Frank, "Numerical Modeling of the Additive Manufacturing 

(AM) Processes of Titanium Alloy, in Titanium Alloys," Towards Achieving Enhanced 

Properties for Diversified Applications, ed. A. K. M. Nurul Amin, Rijeka, Croatia, 

InTech, 2012, pp. 3–28. 

20. Daneshkhah, R., Najafi, M., and Torabian, H., "Numerical Simulation of Weld Pool 

Shape during Laser Beam Welding," International Research Journal of Applied an 

dBasic Sciences, Vol. 3, No. 8, 2012, pp. 1624–1630. 

21. Zhang, W., Liu, Y., Wang, X., and Zhang,Y. M., "Chracterization of Three–dimensional 

Weld Pool Surface in GTAW," Welding Journal,Vol. 91, No. 7, July 2012, pp. 195S–

203S. 

22. Manvatkar, V. D., Gokhale, A. A., Reddy, G. Jagan, Venkataramana, A., and De, A., 

"Estimation of Melt Pool Dimensions, Thermal Cycle, and Hardness Distribution in the 

Laser-engineered Net Shaping Process of Austenitic Stainless Steel," Metallurgical and 
20 

Materials Transactions A—Physical Metallurgy and Materials Science,Vol. 42, No. 13, 

December 2011, pp. 4080–4087. 

23. Rai, R., Palmer, T. A., Elmer, J. W., and DebRoy, T., "Heat Transfer and Fluid Flow 

during Electron Beam Welding of 304L Stainless Steel Alloy," Welding Journal,Vol. 88, 

No. 3, March 2009, pp. 54S–61S. 

24. Rai, R., Burgardt, P., Milewski, J. O., Lienert, T. J., and DebRoy, T., "Heat Transfer and 

Fluid Flow during Electron Beam Welding of 21Cr–6Ni–9Mn Steel and Ti–6Al–4V 

Alloy," Journal of Physics D—Applied Physics, Vol. 42, No. 2, 2009. 

25. Rai, R., Kelly, S. M., Martukanitz, R. P., and DebRoy, T. , "A Convective Heat-transfer 

Model for Partial and Full Penetration Keyhole Mode Laser Welding of a Structural 

Steel," Metallurgical and Materials Transactions A—Physical Metallurgy and Materials 

Science,Vol. 39A, No. 1, January 2008, pp. 98–112. 

26. Rai, R., Roy, G. G., and DebRoy, T., "A Computationally Efficient Model of Convective 

Heat Transfer and Solidification Characteristics during Keyhole Mode Laser Welding," 

Journal of Applied Physics,Vol. 101, No. 5, 2007. 

27. Vasinonta, A., Beuth, J. L., and Griffith, M., "Process Maps for Predicting Residual 

Stress and Melt Pool Size in the Laser-based Fabrication of Thin-walled Structures," 

Journal of Manufacturing Science and Engineering–Transactions of the Asme,Vol. 129, 

No. 1, 2007, pp. 101–109. 

28. Vasinonta, A., Beuth, J. L., and Griffith, M. L., "A Process Map for Consistent Build 

Conditions in the Solid Freeform Fabrication of Thin-walled Structures," Journal of 

Manufacturing Science and Engineering-Transactions of the Asme,Vol. 123, No. 4, 2001, 

pp. 615–622. 

29. Vasinonta, A., Beuth, J. L., and Ong, R., "Melt Pool Size Control in Thin-walled and 

Bulky Parts via Process Maps," Proc. Solid Freeform Fabrication Symposium, Austin, 

2001. 

30. Vasinonta, A., Beuth, J., and Griffith, M., "Process Maps for Controlling Residual Stress 

and Melt Pool Size in Laser-based SFF Processes," Proc. Solid Freeform Fabrication 

Symposium, Austin, 2000. 

31. Zhang, Jingwei, Liou, Frank, Seufzer, William, Newkirk, Joseph, Fab, Zhiqiang, Liu, 

Heng, Sparks, Todd E., "Probabilistic Simulation of Solidification Microstructure 

Evolution during Laser-based Metal Deposition," 24th Annual International Solid 

Freeform Fabrication Symposium, Austin, 2013. 

32. Eshraghi, Mohsen, Felicelli, Sergio D., and Jelinek, Bohumir, "Three Dimensional 

Simulation of Solutal Dendrite Growth Using Lattice Boltzmann and Cellular Automaton 

Methods," Journal of Crystal Growth,Vol. 354, No. 1, 1 September 2012, pp. 129–134. 
21 

33. Eshraghi, M. and Felicelli, S. D., "An Implicit Lattice Boltzmann Model for Heat 

Conduction with Phase Change," International Journal of Heat and Mass Transfer,Vol. 

55, No. 9–10, 2012, pp. 2420–2428. 

34. Barrales-Mora, L. A., Gottstein, G., and Shvindlerman, L. S., "Effect of a Finite 

Boundary Junction Mobility on the Growth Rate of Grains in Two–dimensional 

Polycrystals," Acta Materialia,Vol. 60, No. 2, 2012, pp. 546–555. 

35. Amoorezaei, M., Gurevich, S., and Provatas, N., "Orientation Selection in Solidification 

Patterning," Acta Materialia,Vol. 60, No. 2, 2012, pp. 657–663. 

36. Brandl, Erhard, Michailov, Vesselin, Viehweger, Bernd, and Leyens, Christoph, 

"Deposition of Ti–6Al–4V Using Laser and Wire, Part I: Microstructural Properties of 

Single Beads," Surface & Coatings Technology,Vol. 206, No. 6, 15 December 2011, pp. 

1120–1129. 

37. Groeber, M. A., "Digital Representation of Materials Grain Structure," Computational 

Methods for Microstructure-Property Relationships, ed. Somnath Ghosh and Dennis 

Dimiduk, United States: Springer, 2010, pp. 53–97. 

38. Barrales-Mora, L. A., Gottstein, G., and Shvindlerman, L. S., "Three–dimensional Grain 

Growth: Analytical Approaches and Computer Simulations," Acta Materialia,Vol. 56, 

No. 20, 2008, pp. 5915–5926. 

39. Bontha, Srikanth, Klingbeil, Nathan W., Kobryn, Pamela A., and Fraser, Hamish L., 

"Thermal Process Maps for Predicting Solidification Microstructure in Laser Fabrication 

of Thin-wall Structures," Journal of Materials Processing Technology,Vol. 178, No. 1–3, 

14 September 2006, pp. 135–142. 

40. Demirel, M. C., Kuprat, A. P., George, D. C. and Rollett, A. D., "Bridging Simulations 

and Experiments in Microstructure Evolution," Physical Review Letters,Vol. 90, No. 1, 9 

January 2003. 

41. Yang, Z., Sista, S., Elmer, J. W., and DebRoy, T., "Three–dimensional Monte Carlo 

Simulation of Grain Growth during GTA Welding of Titanium," Acta Materialia,Vol. 48, 

No. 20, 4 December 2000, pp. 4813–4825. 

42. Lindgren, Lars-Erik, Lundbåck, Andreas, and Fisk, M., "Thermo-Mechanics and 

Microstructure Evolution in Manufacturing Simulations," Journal of Thermal 

Stresses,Vol. 36, No. 6, 2013, pp. 564–588. 

43. Ding, J.,Colegrove, P., Mehnen, J., Ganguly, S., Almeida, P. M. Sequeira, Wang, F., 

Williams, S., "Thermo-mechanical Analysis of Wire and Arc Additive Layer 

Manufacturing Process on Large Multi-layer Parts," Computational Materials 

Science,Vol. 50, No. 12, December 2011, pp. 3315–3322. 

44. De, A. and DebRoy, T., "A Perspective on Residual Stresses in Welding," Science and 

Technology of Welding and Joining,Vol. 16, No. 3, April 2011, pp. 204–208. 
22 

45. Tian, Yanhong, Wang, Chunqing, Zhu, Danyang, and Zhou, Y., "Finite Element 

Modeling of Electron Beam Welding of a Large Complex Al Alloy Structure by Parallel 

Computations," Journal of Materials Processing Technology,Vol. 199, No. 1–3, 1 April 

2008, pp. 41–48. 

46. Roy, Joydeep, Majumder, Arindam, Barma, J. D., Rai, R. N., and Saha, S. C., "An 

Approach for Solving Multi Characteristics Optimization of Submerged Arc Welding 

Process Parameters by Using Grey Based Genetic Algorithm," Journal of Scientific & 

Industrial Research,Vol. 72, No. 6, June 2013, pp. 340–347. 

47. Brandl, Erhard, Michailov, Vesselin, Viehweger, Bernd, and Leyens, Christoph, 

"Deposition of Ti–6Al–4V Using Laser and Wire, Part II: Hardness and Dimensions of 

Single Beads," Surface & Coatings Technology,Vol. 206, No. 6, 15 December 2011, pp. 

1130–1141. 

48. Robertson, Ian M., Schuh, Christopher A., Vetrano, John S., Browning, Nigel D., Field, 

David P., Jensen, Dorte Juul, Miller, Michael K., Baker, Ian, Dunand, David C., DuninBorkowski, Rafal, Kabius, Bernd, Kelly, Tom, Lozano-Perez, Sergio, Misra, Amit, 

Rohrer, Gregory S., Rollett, Anthony D., Taheri, Mitra L., Thompson, Greg B., Uchic, 

NMichael, Wang, Xun-Li, and Was, Gary, "Towards an Integrated Materials 

Characterization Toolbox," Journal of Materials Research,Vol. 26, No. 11, 7 June 2011, 

pp. 1341–1383. 

49. Guo, Z., Saunders, N., Schillé, J. P., and Miodownik, A. P., "Material Properties for 

Process Simulation," Materials Science and Engineering A—Structural Materials 

Properties Microstructure and Processing,Vol. 499, No. 1–2, January 2009, pp. 7–13. 

50. Tong, Mingming, Duggan, Gregory, Liu, Jun, Xie, Yu, Dodge, Mike, Aucott, Lee, Dong, 

Hongbiao, Davidchack, Ruslan L., Dantzig, Jon, Barrera, Olga, Cocks, Alan C. F., 

Kitaguchi, Hiroto, Lozano-Perez, Sergio, Zhao, Chaungxin, Richardson, Ian, Kidess, 

Anton, Kleijn, Chris R., Wen, Shuwen, Barnett, Roger, and Browne, David J., 

"Multiscale, Multiphysics Numerical Modeling of Fusion Welding with Experimental 

Characterization and Validation," Journal of Minerals, Metals and Materials Society,Vol. 

65, No. 1, January 2013, pp. 99–106. 

51. Lundbåck, Andreas and Lindgren, Lars-Erik, "Modelling of Metal Deposition," Finite 

Elements in Analysis and Design,Vol. 47, No. 10, October 2011, pp. 1169–1177. 

52. Fallah, Vahid, Alimardani, Masoud, Corbin Stephen F., and Khajepour, Amir, "Temporal 

Development of Melt-pool Morphology and Clad Geometry in Laser Powder 

Deposition," Computational Materials Science,Vol. 50, No. 7, May 2011, pp. 2124–

2134. 

53. Bag, S. and De, A., "Probing Reliability of Transport Phenomena Based Heat Transfer 

and Fluid Flow Analysis in Autogeneous Fusion Welding Process," Metallurgical and 

Materials Transactions A—Physical Metallurgy and Materials Science,Vol. 41, No. 9, 

September 2010, pp. 2337–2347. 
23 

54. Bag, S., De, A., and DebRoy, T., "A Genetic Algorithm-Assisted Inverse Convective 

Heat Transfer Model for Tailoring Weld Geometry," Materials and Manufacturing 

Processes,Vol. 24, No. 3, 18 February 2009, pp. 384–397. 

55. Zhang, W. J. and Zhang, Y. M., "Modeling of Human Welder Response to 3–D Weld 

Pool Surface: Part I–Principles," Welding Journal,Vol. 91, No. 11, November 2012, pp. 

310S–318S. 

56. Zhang, W. J. and Zhang, Y. M., "Modeling of Human Welder Response to 3–D Weld 

Pool Surface: Part II–Results and Analysis," Welding Journal,Vol. 91, No. 12, December 

2012, pp. 329S–337S. 

57. Heralić, Almir, Christiansson, Anna-Karin, and Lennartson, Bengt, "Height Control of 

Laser Metal-wire Deposition Based on Iterative Learning Control and 3–D Scanning," 

Optics and Lasers in Engineering,Vol. 50, No. 9, September 2012, pp. 1230–1241. 

58. Tang, Lie and Landers, Robert G., "Layer-to-Layer Height Control for Laser Metal 

Deposition Process," Journal of Manufacturing Science and Engineering–Transactions of 

the ASME,Vol. 133, No. 2, 15 March 2011. 

59. Cohen, Daniel L. and Lipson, Hod, "Geometric Feedback Control of Discrete-deposition 

SFF Systems," Rapid Prototyping Journal,Vol. 16, No. 5, 2010, pp. 377–393. 

60. Chandrasekhar, N. and Vasudevan, M., "Intelligent Modeling for Optimization of A-TIG 

Welding Process," Materials and Manufacturing Processes,Vol. 25, No. 11, 17 

December 2010, pp. 1341–1350. 
REPORT DOCUMENTATION PAGE Form Approved

OMB No. 0704-0188

2. REPORT TYPE 

Technical Memorandum

 4. TITLE AND SUBTITLE

Additive Manufacturing Modeling and Simulation - A Literature Review 

for Electron Beam Free Form Fabrication 

5a. CONTRACT NUMBER

 6. AUTHOR(S)

Seufzer, William J.

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

NASA Langley Research Center

Hampton, VA 23681-2199

 9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES)

National Aeronautics and Space Administration

Washington, DC 20546-0001

 8. PERFORMING ORGANIZATION

 REPORT NUMBER

L-20391

10. SPONSOR/MONITOR'S ACRONYM(S)

NASA

13. SUPPLEMENTARY NOTES

12. DISTRIBUTION/AVAILABILITY STATEMENT

Unclassified - Unlimited

Subject Category 61

Availability: NASA CASI (443) 757-5802

19a. NAME OF RESPONSIBLE PERSON

STI Help Desk (email: help@sti.nasa.gov)

14. ABSTRACT

Additive manufacturing is coming into industrial use and has several desirable attributes. Control of the deposition remains a 

complex challenge, and so this literature review was initiated to capture current modeling efforts in the field of additive 

manufacturing. This paper summarizes about 10 years of modeling and simulation related to both welding and additive 

manufacturing. The goals were to learn who is doing what in modeling and simulation, to summarize various approaches taken 

to create models, and to identify research gaps. Later sections in the report summarize implications for closed-loop-control of

the process, implications for local research efforts, and implications for local modeling efforts. 

15. SUBJECT TERMS

Additive manufacturing; Electron beam; Literature review; Modeling; Simulation

18. NUMBER

 OF 

 PAGES

33

19b. TELEPHONE NUMBER (Include area code)

(443) 757-5802

a. REPORT

U

c. THIS PAGE

U

b. ABSTRACT

U

17. LIMITATION OF 

 ABSTRACT

UU

Prescribed by ANSI Std. Z39.18

Standard Form 298 (Rev. 8-98)

3. DATES COVERED (From - To)

5b. GRANT NUMBER

5c. PROGRAM ELEMENT NUMBER

5d. PROJECT NUMBER

5e. TASK NUMBER

5f. WORK UNIT NUMBER

473452.02.07.04.01.02 

11. SPONSOR/MONITOR'S REPORT

 NUMBER(S)

NASA/TM-2014-218245

16. SECURITY CLASSIFICATION OF:

The public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, 

gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this 

collection of information, including suggestions for reducing this burden, to Department of Defense, Washington Headquarters Services, Directorate for Information Operations and 

Reports (0704-0188), 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person 

shall be subject to any penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number.

PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS.

1. REPORT DATE (DD-MM-YYYY)

01- 04 - 2014