# Effect of Process Parameters on Short Fiber Orientation along the Melt Flow Direction in Water-Assisted Injection Molded Part

Authors: Haiying Zhou; Hesheng Liu; Qingsong Jiang; Tangqing Kuang; Zhixin Chen; Weiping Li
Journal: Advances in Materials Science and Engineering
 (2019)

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## Abstract

The short fiber orientation (SFO) distribution in the water-assisted injection molding (WAIM) is more complicated than that in traditional injection molding due to the new process parameters. In this work, an improved fiber orientation tensor method was used to simulate the SFO in WAIM. The result was compared with the scanning electron micrograph, which was consistent with the experiments. The effect of six process parameters, including filling time, melt temperature, mold temperature, delay time, water pressure, and water temperature, on the SFO along the melt flow direction were studied through orthogonal experimental design, range analysis, and variance analysis. An artificial neural network was used to establish the nonlinear agent model between the process parameters andA11 representing the fiber orientation in melt flow direction. Results show that water pressure, melt temperature, and water temperature have significant effects on SFO. The three-dimensional (3D) response surfaces and contour plots show that the values of A11 decrease with the increase in water pressure and melt temperature and increase as the water temperature rises.


---

## Body

## 1. Introduction

With the development of advanced, economical, and environmentally friendly society, higher requirements are placed on the performance of plastic products. The short-fiber-reinforced polymer composite (SFRPC) is a material with a polymer as a matrix and short fibers as a dispersed phase. Its characteristics are lightweight, high specific strength and specific modulus, stable chemical properties, heat resistance, and good wear resistance [1]. In recent years, SFRPCs have gradually replaced metal materials in some fields, making them widely used in aviation, automotive, shipping, and medicine [2, 3].Fluid-assisted injection molding is an emerging process that includes gas-assisted injection molding (GAIM) and water-assisted injection molding (WAIM) [4, 5]. The fluids used in GAIM and WAIM are nitrogen and water, respectively. Due to incompressibility, high heat capacity, and good thermal conductivity of water, the advantages of WAIM over GAIM are high product efficiency, more uniform and thinner residual wall thickness (RWT) [6]. Based on whether or not the cavity is completely filled with melt, WAIM can be categorized into two types: short-shot WAIM and overflow WAIM. In short-shot WAIM, the mold cavity is partially filled with melt, followed by the injection of water into the core of melt. In overflow WAIM, the mold cavity is completely filled with melt, followed by the injection of water that pushes the melt into the overflow cavity to form a part with a hollow cross section. Compared with the standard injection molding, WAIM offers significant advantages in the preparation of shaped hollow plastic parts with uniform RWT. However, due to the difficulty in controlling the water injection pressure and the turbulence characteristics of the injection water, the quality of the products is not stable [7, 8]. Present researches on WAIM focus on the distribution of RWT [9], the length of water column penetration [10, 11], and the defects of molded parts [12, 13].The RWT of water-assisted injection molded parts is thin, and the mechanical properties of the parts can be greatly improved by using SFRPC as a raw material. Many studies reported that the distribution of short fiber orientation (SFO) determined the mechanical and physical properties of the plastic parts, while fiber orientation was affected by mold structure, molding process parameters, flow field distribution, initial state of fibers, fiber properties, and matrix properties [14–16]. The molding process parameters influence temperature, velocity distribution, melt viscosity gradient, and flow field, which ultimately determine the fiber orientation. Liu et al. [7, 12] found that the short fibers mostly aligned along the melt flow direction in WAIM. Huang et al. [17] suggested that high-pressure water penetration significantly affected fiber orientation in WAIM, and increasing melt temperature decreased fiber orientation. Systematic studies on the influence of process parameters on the SFO help understand the fiber orientation mechanism, optimize the SFO distribution, and improve the overall performance of the parts in WAIM.WAIM, including the melt and high-pressure water filling stages, is more complicated than the standard injection molding process. Due to the difficultly in accurately controlling all the process parameters simultaneously, the research of SFO in laboratory is performed for qualitative analysis. With the development of computer technology, the three-dimensional numerical simulation technology developed rapidly, enabling simulating complex injection molding. The process parameters can be accurately controlled in simulation, but the reliability of SFO simulation depends on the accuracy of the mathematical model. The fiber orientation distribution in injection molding is very complicated microscopically. In the past three decades, theoretical studies on fibers suspension rheology have achieved a great success. Based on the classic fiber orientation models, including Jeffery hydrodynamics model, Folgar–Tucker model [18], and ARD-RSC model [19], Tseng et al. recently proposed an improved iARD-RPR model [20, 21], which can provide good simulation results of SFO in standard injection molding.In this work, based on the iARD-RPR model, the SFO in WAIM was simulated, and the results were compared with the scanning electron micrographs (SEMs) to verify the applicability of this model for WAIM. The influences of process parameters on the fiber orientation along the melt flow direction were studied through the methods of orthogonal experimental design, range analysis, and variance analysis. The nonlinear proxy model between process parameters and fiber orientation along the melt flow direction was constructed by artificial neural network (ANN), and the interaction effect of significant process parameters was investigated using 3D response surfaces and contour plots.


## 2. Methods

### 2.1. Fluid Mechanics Governing Equations

The movement of short fibers in injection molding is a transient, non-Newtonian, and nonisothermal process. In numerical simulations, the melt is considered to be incompressible and laminar, and the inertial term is ignored. The governing equations for transient and nonisothermal fluid motion in WAIM processes are as follows:(1)∂ρ∂t+∇⋅ρu=0,∂∂tρu+∇⋅ρuu−σ=ρg,σ=−pI+η∇u+∇uT,ρCp∂T∂t+u⋅∇T=∇⋅k∇T+ηγ˙2,where u is the velocity vector, η is the shear viscosity, p is the pressure, ρ is the melt density, σ is the total stress tensor, g is the gravity, Cp is the specific heat capacity, T is the temperature, k is the thermal conductivity, t is the time, and γ˙ is the shear rate.


### 2.2. Rheological Model

The Cross-WLF rheological model with seven parameters was used in the simulation, which can describe complex viscosity behaviors, including the viscosity varying with shear rate and the zero-shear-rate viscosity, depending on temperature and pressure [20].(2)ηγ˙,T,P=η0T,P1+η0γ˙/τ∗1−n,η0T,P=D1exp−A1T−TcA2+T−Tc,Tc=D2+D3P,A2=A˜2+D3⋅P,where η is the viscosity of the melt, η0 is the zero shear viscosity, γ˙ is the shear rate, τ∗ is the material constant, n is the power law index in the shear rate, T is the melt temperature, Tc is the glass transition temperature, and D1, D2, D3, A1, and A˜2 are the constants associated with the selected material.


### 2.3. Fiber Orientation Model

The fiber orientation is often described by two methods including orientation probability density distribution function and orientation tensor. The orientation probability density distribution function is used to calculate the ratio of short fibers to the total number of fibers in a certain orientation direction. It is intuitive and easy to understand, but its wide application is limited due to the large amount of calculation. In this work, the numerical simulation of SFO in WAIM is based on the fiber orientation tensor evolution equation. To succinctly represent the orientation of a large number of fibers, a second-order orientation tensor is defined as follows [18]:(3)A=∮ψPPPdP=A11A12A13A12A22A23A13A23A33,A11+A22+A33=1,where ψP is the probability density distribution function of the whole orientation space, P is the fiber’s unit vector, A is a symmetric matrix, and when A = I/3, it represents the orientation state isotropic, where I represents an identity matrix. The three components A11, A22, and A33 on the diagonal represent the fiber orientation in the melt flow direction, the cross-flow direction, and the thickness direction, respectively.Tseng recently proposed a new fiber orientation prediction model (iARD-RPR) [21] as below:(4)A˙=A˙HD+A˙iARDCI,CM+A˙RPRα,where A˙HD is a Jeffery hydrodynamic model, A˙iARD is an improved anisotropic rotational diffusion model with two effective parameters: fiber-fiber interaction factor CI0<CI<0.1 and fiber-matrix interaction influence factor CM0<CM<1, and A˙RPR is the retarding principal rate model that contains the parameter α (0 < α < 1), which is mean to slow the response rate of fiber orientation. The orientation tensor will be influenced by the parameters, and the default parameters (CI = 0.005, CM = 0.1, and α = 0.7) of iARD-RPR model for short fibers were used in the simulations:(5)A˙HD=W⋅A−A⋅W+ξD⋅A+A⋅D−2A4:D,where W is the vortex tensor, D is the deformation rate tensor, ξ is the particle shape factor, and the fourth-order orientation tensor A4 can be estimated from A by using the eigenvalue-based optimal fitting (EBOF) closure [22] or the invariant-based optimal fitting (IBOF) closure [23]. EBOF was chosen in this paper.(6)A˙iARD=γ˙2Dr−2trDrA−5Dr⋅A−5A⋅Dr+10A4:Dr,Dr=CII−CMD2‖D2‖,A˙RPR=−R⋅Λ˙IOK⋅RT,Λ˙iiIOK=αλ˙i,i,j,k=1,2,3,where Λ˙IOK is a material derivative of a diagonal tensor and its superscript is the intrinsic orientation kinetic (IOK) assumption, Λ˙iiIOK is the i-th diagonal component of Λ˙IOK, R is a rotation matrix, λi is the eigenvalue of matrix A, and λ1>λ2>λ3.In the simulation, the melt flow problem was solved firstly, and then the resulting velocity field was applied to compute the short fiber orientation.


### 2.4. Geometric Model

The geometric model is shown in Figure1. The length of the functional hollow duct is 245 mm, and the diameter is 20 mm. The overflow cavity was used to accommodate the melt pushed out by the high-pressure water. The connecting pipe between the duct and the overflow cavity had a diameter of 10 mm. The 3D model was created using Pro/E software. Moldex3D, developed by CoreTech System Co. Ltd., was used to simulate the melt filling and high-pressure water penetration process of WAIM. The combined model, imported into the Moldex3D R15.0, was meshed with Boundary Layer Mesh format. In the simulation, a short-fiber-reinforced PP (Fiberfil J-68/30/E with a short grass fiber mass fraction of 30% and an aspect ratio of 20) was selected as material, and its properties were available in the data bank of Moldex3D. The simulation of overflow WAIM process was carried out. First, the melt was injected into the mold cavity of the functional plastic part. Second, after a short delay time, the high-pressure water was injected into the core of the melt and penetrated along the core position with the least flow resistance and pushed the core melt into the overflow cavity to form a plastic part with a hollow cross section.Figure 1
Geometric model for the numerical simulation experiment.


### 2.5. Orthogonal Experimental Design

During the injection molding process, due to the differences in viscosity gradient and velocity field distribution, the melt was sheared and stretched by the surrounding fluid, the shearing action causes the short fibers to align along the melt flow direction, and the stretching action causes the short fibers to orient along the stretch direction. Many factors affect the melt flow field in WAIM. The process parameters considered in this study include filling time (A), melt temperature (B), mold temperature (C), delay time (D), water pressure (E), and water temperature (F). These factors and their corresponding levels are listed in Table 1.Table 1
Process parameters and levels of orthogonal experiment design.
Process parameters
Levels
1
2
3
4
5
Filling time (A) (s)
1
1.4
1.8
2.2
2.6
Melt temperature (B) (°C)
220
230
240
250
260
Mold temperature (C) (°C)
40
50
60
70
80
Delay time (D) (s)
1
1.5
2
2.5
3
Water pressure (E) (MPa)
6
6.5
7
7.5
8
Water temperature (F) (°C)
20
25
30
35
40The full factorial experimental design method demands 56 experiments for 6 factors (A, B, C, D, E, and F) with 5 levels. To reduce the number of experiments and comprehensively examine the influence of process parameters on SFO, the orthogonal experimental design (L25 (56)) was used to arrange the test plan. In the final stage, the short fibers were mainly oriented along the melt flow direction. The values of the component of orientation tensor representing the melt flow direction were got at twenty different points along the thickness direction at the position I of RWT, and objective A11 is the average value of these points (Table 2).Table 2
Orthogonal experimental arrangement and results of simulation.
No.
A
B
C
D
E
F
A11
1
1
1
1
1
1
1
0.608
2
1
2
2
2
2
2
0.594
3
1
3
3
3
3
3
0.553
4
1
4
4
4
4
4
0.553
5
1
5
5
5
5
5
0.559
6
2
1
2
3
4
5
0.588
7
2
2
3
4
5
1
0.557
8
2
3
4
5
1
2
0.585
9
2
4
5
1
2
3
0.556
10
2
5
1
2
3
4
0.562
11
3
1
3
5
2
4
0.64
12
3
2
4
1
3
5
0.584
13
3
3
5
2
4
1
0.556
14
3
4
1
3
5
2
0.54
15
3
5
2
4
1
3
0.576
16
4
1
4
2
5
3
0.549
17
4
2
5
3
1
4
0.639
18
4
3
1
4
2
5
0.63
19
4
4
2
5
3
1
0.549
20
4
5
3
1
4
2
0.537
21
5
1
5
4
3
2
0.585
22
5
2
1
5
4
3
0.569
23
5
3
2
1
5
4
0.555
24
5
4
3
2
1
5
0.573
25
5
5
4
3
2
1
0.545


### 2.6. Range Analysis

Range analysis, a statistical method, was used to analyze the experimental results obtained in the orthogonal experimental design. Range is defined as the difference between the maximum value and the minimum value. The factor with the maximum range is regarded as the most sensitive process parameter.(7)Kij=∑i=1p1yijk,Rj=maxK1j,K2j,…,K5j−minK1j,K2j,…,K5j,where i (i = 1, 2, 3, 4, 5) is the level of a factor, j (j = A, B, C, D, E, and F) is a certain factor, Kij is the sum value of A11 of all level in each factor, yijk is the value of the k-th experimental result, p1 is the test times with the j-th factor and the i-th level, and range Rj represents the influence degree of the factor. The larger the range Rj is, the stronger the influence of the factor is.


### 2.7. Analysis of Variance

Analysis of variance was employed to determine which factors were significant. In this method, theF value of each factor is used to indicate the degree of influence.(8)T=∑i=1n∑k=1p1yijk,Ssum=∑i=1n∑k=1pyijk2−T2p0,Sj=∑i=1nKij2p1−T2p0,Se=Ssum−∑j=1mSj,F=Sj/fjSe/fe,where T is the sum of test indexes, n is the number of levels, m is the number of factors, Ssum is the total differences, Sj is the differences of test results caused by the change in every level of factor j, Se is the differences of the test caused by the error, p0 is the total test times, fj and fe are the degrees of freedom of each factor and test error, respectively, and other symbols are the same as above.Given a significant levelα, the criterion Fαfj,fe can be found in the F distribution table. The influence of a factor with F value greater than Fα value is significant.


### 2.8. Artificial Neural Network (ANN)

ANN inspired by the biologic neural system is a computing model used to map linear or nonlinear relationships between factors and responses. ANN can work as a human brain to establish a sample model from the perspective of information processing, without prior information or heuristic assumptions. An ANN model comprises three parts: one input layer, one or more hidden layers, and one output layer. The numbers of neurons in the input and output layer are determined by the numbers of factors and responses, respectively. In general, the number of neurons in hidden layer is determined by trial and error.According to Kolmogorov’s theorem [24], an ANN model with a single hidden layer has the ability to map any complex nonlinear relationship between input and output. In this study, an ANN model with one hidden layer was used for modeling. Gradient descent algorithm was used for training model. The transfer functions of Tansig and Purelin were employed in the hidden and output layer, respectively. By changing the number of neurons in the hidden layer from 5 to 15, the ANN topology of 6-13-1 with the minimum mean square error between the targets and the outputs was determined, indicating that there were six neurons in the input layer, thirteen neurons in the hidden layer, and one neuron in the output layer (Figure 2).Figure 2
Topology of error back-propagation ANN.


## 2.1. Fluid Mechanics Governing Equations

The movement of short fibers in injection molding is a transient, non-Newtonian, and nonisothermal process. In numerical simulations, the melt is considered to be incompressible and laminar, and the inertial term is ignored. The governing equations for transient and nonisothermal fluid motion in WAIM processes are as follows:(1)∂ρ∂t+∇⋅ρu=0,∂∂tρu+∇⋅ρuu−σ=ρg,σ=−pI+η∇u+∇uT,ρCp∂T∂t+u⋅∇T=∇⋅k∇T+ηγ˙2,where u is the velocity vector, η is the shear viscosity, p is the pressure, ρ is the melt density, σ is the total stress tensor, g is the gravity, Cp is the specific heat capacity, T is the temperature, k is the thermal conductivity, t is the time, and γ˙ is the shear rate.


## 2.2. Rheological Model

The Cross-WLF rheological model with seven parameters was used in the simulation, which can describe complex viscosity behaviors, including the viscosity varying with shear rate and the zero-shear-rate viscosity, depending on temperature and pressure [20].(2)ηγ˙,T,P=η0T,P1+η0γ˙/τ∗1−n,η0T,P=D1exp−A1T−TcA2+T−Tc,Tc=D2+D3P,A2=A˜2+D3⋅P,where η is the viscosity of the melt, η0 is the zero shear viscosity, γ˙ is the shear rate, τ∗ is the material constant, n is the power law index in the shear rate, T is the melt temperature, Tc is the glass transition temperature, and D1, D2, D3, A1, and A˜2 are the constants associated with the selected material.


## 2.3. Fiber Orientation Model

The fiber orientation is often described by two methods including orientation probability density distribution function and orientation tensor. The orientation probability density distribution function is used to calculate the ratio of short fibers to the total number of fibers in a certain orientation direction. It is intuitive and easy to understand, but its wide application is limited due to the large amount of calculation. In this work, the numerical simulation of SFO in WAIM is based on the fiber orientation tensor evolution equation. To succinctly represent the orientation of a large number of fibers, a second-order orientation tensor is defined as follows [18]:(3)A=∮ψPPPdP=A11A12A13A12A22A23A13A23A33,A11+A22+A33=1,where ψP is the probability density distribution function of the whole orientation space, P is the fiber’s unit vector, A is a symmetric matrix, and when A = I/3, it represents the orientation state isotropic, where I represents an identity matrix. The three components A11, A22, and A33 on the diagonal represent the fiber orientation in the melt flow direction, the cross-flow direction, and the thickness direction, respectively.Tseng recently proposed a new fiber orientation prediction model (iARD-RPR) [21] as below:(4)A˙=A˙HD+A˙iARDCI,CM+A˙RPRα,where A˙HD is a Jeffery hydrodynamic model, A˙iARD is an improved anisotropic rotational diffusion model with two effective parameters: fiber-fiber interaction factor CI0<CI<0.1 and fiber-matrix interaction influence factor CM0<CM<1, and A˙RPR is the retarding principal rate model that contains the parameter α (0 < α < 1), which is mean to slow the response rate of fiber orientation. The orientation tensor will be influenced by the parameters, and the default parameters (CI = 0.005, CM = 0.1, and α = 0.7) of iARD-RPR model for short fibers were used in the simulations:(5)A˙HD=W⋅A−A⋅W+ξD⋅A+A⋅D−2A4:D,where W is the vortex tensor, D is the deformation rate tensor, ξ is the particle shape factor, and the fourth-order orientation tensor A4 can be estimated from A by using the eigenvalue-based optimal fitting (EBOF) closure [22] or the invariant-based optimal fitting (IBOF) closure [23]. EBOF was chosen in this paper.(6)A˙iARD=γ˙2Dr−2trDrA−5Dr⋅A−5A⋅Dr+10A4:Dr,Dr=CII−CMD2‖D2‖,A˙RPR=−R⋅Λ˙IOK⋅RT,Λ˙iiIOK=αλ˙i,i,j,k=1,2,3,where Λ˙IOK is a material derivative of a diagonal tensor and its superscript is the intrinsic orientation kinetic (IOK) assumption, Λ˙iiIOK is the i-th diagonal component of Λ˙IOK, R is a rotation matrix, λi is the eigenvalue of matrix A, and λ1>λ2>λ3.In the simulation, the melt flow problem was solved firstly, and then the resulting velocity field was applied to compute the short fiber orientation.


## 2.4. Geometric Model

The geometric model is shown in Figure1. The length of the functional hollow duct is 245 mm, and the diameter is 20 mm. The overflow cavity was used to accommodate the melt pushed out by the high-pressure water. The connecting pipe between the duct and the overflow cavity had a diameter of 10 mm. The 3D model was created using Pro/E software. Moldex3D, developed by CoreTech System Co. Ltd., was used to simulate the melt filling and high-pressure water penetration process of WAIM. The combined model, imported into the Moldex3D R15.0, was meshed with Boundary Layer Mesh format. In the simulation, a short-fiber-reinforced PP (Fiberfil J-68/30/E with a short grass fiber mass fraction of 30% and an aspect ratio of 20) was selected as material, and its properties were available in the data bank of Moldex3D. The simulation of overflow WAIM process was carried out. First, the melt was injected into the mold cavity of the functional plastic part. Second, after a short delay time, the high-pressure water was injected into the core of the melt and penetrated along the core position with the least flow resistance and pushed the core melt into the overflow cavity to form a plastic part with a hollow cross section.Figure 1
Geometric model for the numerical simulation experiment.


## 2.5. Orthogonal Experimental Design

During the injection molding process, due to the differences in viscosity gradient and velocity field distribution, the melt was sheared and stretched by the surrounding fluid, the shearing action causes the short fibers to align along the melt flow direction, and the stretching action causes the short fibers to orient along the stretch direction. Many factors affect the melt flow field in WAIM. The process parameters considered in this study include filling time (A), melt temperature (B), mold temperature (C), delay time (D), water pressure (E), and water temperature (F). These factors and their corresponding levels are listed in Table 1.Table 1
Process parameters and levels of orthogonal experiment design.
Process parameters
Levels
1
2
3
4
5
Filling time (A) (s)
1
1.4
1.8
2.2
2.6
Melt temperature (B) (°C)
220
230
240
250
260
Mold temperature (C) (°C)
40
50
60
70
80
Delay time (D) (s)
1
1.5
2
2.5
3
Water pressure (E) (MPa)
6
6.5
7
7.5
8
Water temperature (F) (°C)
20
25
30
35
40The full factorial experimental design method demands 56 experiments for 6 factors (A, B, C, D, E, and F) with 5 levels. To reduce the number of experiments and comprehensively examine the influence of process parameters on SFO, the orthogonal experimental design (L25 (56)) was used to arrange the test plan. In the final stage, the short fibers were mainly oriented along the melt flow direction. The values of the component of orientation tensor representing the melt flow direction were got at twenty different points along the thickness direction at the position I of RWT, and objective A11 is the average value of these points (Table 2).Table 2
Orthogonal experimental arrangement and results of simulation.
No.
A
B
C
D
E
F
A11
1
1
1
1
1
1
1
0.608
2
1
2
2
2
2
2
0.594
3
1
3
3
3
3
3
0.553
4
1
4
4
4
4
4
0.553
5
1
5
5
5
5
5
0.559
6
2
1
2
3
4
5
0.588
7
2
2
3
4
5
1
0.557
8
2
3
4
5
1
2
0.585
9
2
4
5
1
2
3
0.556
10
2
5
1
2
3
4
0.562
11
3
1
3
5
2
4
0.64
12
3
2
4
1
3
5
0.584
13
3
3
5
2
4
1
0.556
14
3
4
1
3
5
2
0.54
15
3
5
2
4
1
3
0.576
16
4
1
4
2
5
3
0.549
17
4
2
5
3
1
4
0.639
18
4
3
1
4
2
5
0.63
19
4
4
2
5
3
1
0.549
20
4
5
3
1
4
2
0.537
21
5
1
5
4
3
2
0.585
22
5
2
1
5
4
3
0.569
23
5
3
2
1
5
4
0.555
24
5
4
3
2
1
5
0.573
25
5
5
4
3
2
1
0.545


## 2.6. Range Analysis

Range analysis, a statistical method, was used to analyze the experimental results obtained in the orthogonal experimental design. Range is defined as the difference between the maximum value and the minimum value. The factor with the maximum range is regarded as the most sensitive process parameter.(7)Kij=∑i=1p1yijk,Rj=maxK1j,K2j,…,K5j−minK1j,K2j,…,K5j,where i (i = 1, 2, 3, 4, 5) is the level of a factor, j (j = A, B, C, D, E, and F) is a certain factor, Kij is the sum value of A11 of all level in each factor, yijk is the value of the k-th experimental result, p1 is the test times with the j-th factor and the i-th level, and range Rj represents the influence degree of the factor. The larger the range Rj is, the stronger the influence of the factor is.


## 2.7. Analysis of Variance

Analysis of variance was employed to determine which factors were significant. In this method, theF value of each factor is used to indicate the degree of influence.(8)T=∑i=1n∑k=1p1yijk,Ssum=∑i=1n∑k=1pyijk2−T2p0,Sj=∑i=1nKij2p1−T2p0,Se=Ssum−∑j=1mSj,F=Sj/fjSe/fe,where T is the sum of test indexes, n is the number of levels, m is the number of factors, Ssum is the total differences, Sj is the differences of test results caused by the change in every level of factor j, Se is the differences of the test caused by the error, p0 is the total test times, fj and fe are the degrees of freedom of each factor and test error, respectively, and other symbols are the same as above.Given a significant levelα, the criterion Fαfj,fe can be found in the F distribution table. The influence of a factor with F value greater than Fα value is significant.


## 2.8. Artificial Neural Network (ANN)

ANN inspired by the biologic neural system is a computing model used to map linear or nonlinear relationships between factors and responses. ANN can work as a human brain to establish a sample model from the perspective of information processing, without prior information or heuristic assumptions. An ANN model comprises three parts: one input layer, one or more hidden layers, and one output layer. The numbers of neurons in the input and output layer are determined by the numbers of factors and responses, respectively. In general, the number of neurons in hidden layer is determined by trial and error.According to Kolmogorov’s theorem [24], an ANN model with a single hidden layer has the ability to map any complex nonlinear relationship between input and output. In this study, an ANN model with one hidden layer was used for modeling. Gradient descent algorithm was used for training model. The transfer functions of Tansig and Purelin were employed in the hidden and output layer, respectively. By changing the number of neurons in the hidden layer from 5 to 15, the ANN topology of 6-13-1 with the minimum mean square error between the targets and the outputs was determined, indicating that there were six neurons in the input layer, thirteen neurons in the hidden layer, and one neuron in the output layer (Figure 2).Figure 2
Topology of error back-propagation ANN.


## 3. Results and Discussion

### 3.1. Comparison between Simulation Result and Experimental Observation

Figure3 shows the component of orientation tensor along the melt flow direction in a section taken from the position I (Figure 1) of the model used in the simulation with each process parameter set to level 3. The values of A11 first increase and then decrease from the region near the mold cavity to the region near the water channel, and the larger values of A11 indicate more short fibers orient in the flow direction in the corresponding region. Based on the SFO in the flow direction, the RWT can be divided into two regions, such as the ordered region with large A11 and disordered region with small A11. To verify the simulation results, water-assisted injection molded tubes with short-fiber-reinforced composite were observed by scanning electron microscopy (Nova NanoSEM 450) with an accelerating voltage of 5 kV. The tubes were produced by a laboratory-developed WAIM device improved from a 110 -ton traditional injection molding machine. The material was PP containing 30% short grass fibers. The process parameters were set to the same as the simulation. A small sample was taken from the tube and immersed in liquid nitrogen for two hours and then cryofractured along the melt flow direction. The surface of the sample was subjected to gold-sputtered treatment before observation. Figure 4 shows that the short fibers in the RWT mostly orient along the melt flow direction. Figures 4(a) and 4(b), the magnified micrographs taken from the ordered region and disordered region, indicate that more short fibers orient along the melt flow direction in the region near the mold cavity than that near the water channel. The experimental observations are consistent with the simulation results, indicating that the iARD-RPR model can well predict the SFO distribution in overflow WAIM.Figure 3
Contour of A11 in the RWT cross section.Figure 4
SEM micrographs of RWT: (a) ordered region and (b) disordered region.


### 3.2. Analysis of Short Fiber Orientation Mechanism in WAIM

Figure5 shows the values of A11 obtained from 100 points along the diameter in the simulation. The melt filling process was similar to the conventional injection molding process, in which the short fibers oriented with a typical shell-core structure. The short fibers in the shell layer mainly oriented along the melt flow direction due to the shear action, whereas the short fibers in the core layer weakly oriented due to stretch action. The values of A11 increased from about 0.7 to 0.88 within the thickness of 0 to 2 mm. This could be explained that the surface melt temperature decreases rapidly due to the cold mold cavity, and the short fibers are solidified without being sufficiently oriented; as the thickness increases, the cooling effect decreases and A11 increases.Figure 5
Distribution of SFO along the melt direction.In the water filling stage, the values of A11 in the region near the mold cavity are similar to that in the melt filling stage, which are caused by high shear action; the values of A11 decrease rapidly in the region near the water channel, indicating that the high-pressure water filling has an important influence on the SFO in this region. To prevent the occurrence of water vaporization, the water filling process lasts very short, and the water column in a turbulent state exerts strong squeezing and friction at the interface between the melt and water. Furthermore, as the melt moves into the overflow cavity, the moving melt stretches the adjacent melt. The short fibers originally aligned along the flow direction readjust the posture due to the multiple disturbances, making short fibers in a disordered state in the region near water channel. In addition, the components of orientation tensors in all directions were assigned the values of 0.33 in the water channel with no fiber as shown in Figure 5.


### 3.3. Sensitivity Analysis

The results of range analysis are shown in Table3. The rank of sensitivity of A11 to the six selected process parameters was determined and shown in the following based on the magnitude of the range R (water pressure, melt temperature, water temperature, mold temperature, filling rate, and delay time). As shown in Table 4, the results of the variance analysis indicate that the melt temperature and water pressure are highly significant factors for A11 with an F value of 7.346 and 8.612, respectively, and the water temperature with an F value of 4.102 is a significant factor for A11. The filling rate, mold temperature, and delay time are not significant for A11 with the F values of 0.924, 1.163, and 0.913, respectively.Table 3
Results of range analysis.
Factors
A
B
C
D
E
F
K
1j
2.8667
2.97
2.9105
2.8399
2.9812
2.8139
K
2j
2.8475
2.944
2.8624
2.8345
2.9657
2.8405
K
3j
2.8967
2.8784
2.8583
2.8649
2.8322
2.8042
K
4j
2.9043
2.7707
2.8163
2.9005
2.8025
2.949
K
5j
2.8272
2.7794
2.8949
2.9026
2.7609
2.9347
R
0.0772
0.1993
0.0942
0.0681
0.2203
0.1448
Rank
E > B > F > C > A > DTable 4
Results of variance analysis.
Factors
Sum of squares
Degree of freedom
Mean square
F value
F
a
Significant
A
0.845E − 3
4
0.211E − 3
0.924
F
0.05(4, 12) = 3.26
B
0.672E − 2
4
0.168E − 2
7.346
∗∗∗
C
0.106E − 2
4
0.266E − 3
1.163
F
0.01(4, 12) = 5.41
D
0.834E − 3
4
0.208E − 3
0.913
E
0.787E − 2
4
0.197E − 2
8.612
∗∗∗
F
0.375E − 2
4
0.938E − 3
4.102
∗∗
Err.
0.274E − 2
12
0.228E − 3
Sum
0.0211
24
∗∗Significant. ∗∗∗Highly significant.


### 3.4. Artificial Neural Network Modeling

Experimental data (Table2), obtained from orthogonal experimental design, were divided randomly into three data sets. 19 of overall 25 points were used to train the ANN model, and the others (3 + 3) were used to validate and test the model, respectively. The trained neural network was used to predict A11 in the orthogonal experimental scheme. As shown in Figure 6, the training sets and validation sets were almost on the diagonal line, and the test sets were very close to this line, indicating that the neural network has established the nonlinear relationship between the process parameters and A11 and could provide acceptable predictions.Figure 6
Predicted A11 values versus experimental A11 values.


### 3.5. Interaction Effect of Significant Process Parameter

Range and variance analysis results show that melt temperature, water pressure, and water temperature are significant for A11 of RWT in WAIM. The values of A11 were predicted by the trained neural network by changing two process parameters, while keeping the others constant. The 3D response surfaces and contour plots were used to describe the interaction effect of process parameters on A11.As shown in Figures7 and 8, the value of A11 decreases as the melt temperature rises. The reasons come from two aspects. Firstly, the increasing melt temperature results in a decrease in viscosity. At the same shear rate, the shear stress decreases, and this is not conducive to the fiber orientation. Secondly, during the melt filling stage, the polymer molecular chains are also aligned, and the high melt temperature facilitates the deorientation behavior of the molecular chains after the completion of water injection process, which changes the orientation distribution of the short fibers and decreases A11.Figure 7
3D response surface and contour plot of interaction effect of melt temperature and water pressure on A11.Figure 8
3D response surface and contour plot of interaction effect of melt temperature and water temperature on A11.The values of A11 decrease with the increase in the water pressure (Figures 7 and 9). After melt filling stage, the short fibers in the shell layer highly orient along the melt flow direction. During the high-pressure water filling stage, the inner layers of RWT bear the pressing and friction of water column as well as the stretching of the moving melt. The short fibers originally oriented along the melt flow direction are posture-adjusted and arranged in a disordered state. The greater the water pressure is, the stronger the disturbance to the RWT is, and the smaller A11 in the melt flow direction is.Figure 9
3D response surface and contour plot of interaction effect of water pressure and water temperature on A11.While the water injection temperature rises, the values of A11 of RWT in WAIM increase (Figures 8 and 9). The polymer melt has a viscoelastic property and can return to the original state after the external force is removed. The multiple disturbances cause the short fibers to be disordered in the inner layers of RWT during the water filling stage, and the higher water temperature favors the elastic recovery of the short fiber posture, thereby resulting in an increase of A11.


## 3.1. Comparison between Simulation Result and Experimental Observation

Figure3 shows the component of orientation tensor along the melt flow direction in a section taken from the position I (Figure 1) of the model used in the simulation with each process parameter set to level 3. The values of A11 first increase and then decrease from the region near the mold cavity to the region near the water channel, and the larger values of A11 indicate more short fibers orient in the flow direction in the corresponding region. Based on the SFO in the flow direction, the RWT can be divided into two regions, such as the ordered region with large A11 and disordered region with small A11. To verify the simulation results, water-assisted injection molded tubes with short-fiber-reinforced composite were observed by scanning electron microscopy (Nova NanoSEM 450) with an accelerating voltage of 5 kV. The tubes were produced by a laboratory-developed WAIM device improved from a 110 -ton traditional injection molding machine. The material was PP containing 30% short grass fibers. The process parameters were set to the same as the simulation. A small sample was taken from the tube and immersed in liquid nitrogen for two hours and then cryofractured along the melt flow direction. The surface of the sample was subjected to gold-sputtered treatment before observation. Figure 4 shows that the short fibers in the RWT mostly orient along the melt flow direction. Figures 4(a) and 4(b), the magnified micrographs taken from the ordered region and disordered region, indicate that more short fibers orient along the melt flow direction in the region near the mold cavity than that near the water channel. The experimental observations are consistent with the simulation results, indicating that the iARD-RPR model can well predict the SFO distribution in overflow WAIM.Figure 3
Contour of A11 in the RWT cross section.Figure 4
SEM micrographs of RWT: (a) ordered region and (b) disordered region.


## 3.2. Analysis of Short Fiber Orientation Mechanism in WAIM

Figure5 shows the values of A11 obtained from 100 points along the diameter in the simulation. The melt filling process was similar to the conventional injection molding process, in which the short fibers oriented with a typical shell-core structure. The short fibers in the shell layer mainly oriented along the melt flow direction due to the shear action, whereas the short fibers in the core layer weakly oriented due to stretch action. The values of A11 increased from about 0.7 to 0.88 within the thickness of 0 to 2 mm. This could be explained that the surface melt temperature decreases rapidly due to the cold mold cavity, and the short fibers are solidified without being sufficiently oriented; as the thickness increases, the cooling effect decreases and A11 increases.Figure 5
Distribution of SFO along the melt direction.In the water filling stage, the values of A11 in the region near the mold cavity are similar to that in the melt filling stage, which are caused by high shear action; the values of A11 decrease rapidly in the region near the water channel, indicating that the high-pressure water filling has an important influence on the SFO in this region. To prevent the occurrence of water vaporization, the water filling process lasts very short, and the water column in a turbulent state exerts strong squeezing and friction at the interface between the melt and water. Furthermore, as the melt moves into the overflow cavity, the moving melt stretches the adjacent melt. The short fibers originally aligned along the flow direction readjust the posture due to the multiple disturbances, making short fibers in a disordered state in the region near water channel. In addition, the components of orientation tensors in all directions were assigned the values of 0.33 in the water channel with no fiber as shown in Figure 5.


## 3.3. Sensitivity Analysis

The results of range analysis are shown in Table3. The rank of sensitivity of A11 to the six selected process parameters was determined and shown in the following based on the magnitude of the range R (water pressure, melt temperature, water temperature, mold temperature, filling rate, and delay time). As shown in Table 4, the results of the variance analysis indicate that the melt temperature and water pressure are highly significant factors for A11 with an F value of 7.346 and 8.612, respectively, and the water temperature with an F value of 4.102 is a significant factor for A11. The filling rate, mold temperature, and delay time are not significant for A11 with the F values of 0.924, 1.163, and 0.913, respectively.Table 3
Results of range analysis.
Factors
A
B
C
D
E
F
K
1j
2.8667
2.97
2.9105
2.8399
2.9812
2.8139
K
2j
2.8475
2.944
2.8624
2.8345
2.9657
2.8405
K
3j
2.8967
2.8784
2.8583
2.8649
2.8322
2.8042
K
4j
2.9043
2.7707
2.8163
2.9005
2.8025
2.949
K
5j
2.8272
2.7794
2.8949
2.9026
2.7609
2.9347
R
0.0772
0.1993
0.0942
0.0681
0.2203
0.1448
Rank
E > B > F > C > A > DTable 4
Results of variance analysis.
Factors
Sum of squares
Degree of freedom
Mean square
F value
F
a
Significant
A
0.845E − 3
4
0.211E − 3
0.924
F
0.05(4, 12) = 3.26
B
0.672E − 2
4
0.168E − 2
7.346
∗∗∗
C
0.106E − 2
4
0.266E − 3
1.163
F
0.01(4, 12) = 5.41
D
0.834E − 3
4
0.208E − 3
0.913
E
0.787E − 2
4
0.197E − 2
8.612
∗∗∗
F
0.375E − 2
4
0.938E − 3
4.102
∗∗
Err.
0.274E − 2
12
0.228E − 3
Sum
0.0211
24
∗∗Significant. ∗∗∗Highly significant.


## 3.4. Artificial Neural Network Modeling

Experimental data (Table2), obtained from orthogonal experimental design, were divided randomly into three data sets. 19 of overall 25 points were used to train the ANN model, and the others (3 + 3) were used to validate and test the model, respectively. The trained neural network was used to predict A11 in the orthogonal experimental scheme. As shown in Figure 6, the training sets and validation sets were almost on the diagonal line, and the test sets were very close to this line, indicating that the neural network has established the nonlinear relationship between the process parameters and A11 and could provide acceptable predictions.Figure 6
Predicted A11 values versus experimental A11 values.


## 3.5. Interaction Effect of Significant Process Parameter

Range and variance analysis results show that melt temperature, water pressure, and water temperature are significant for A11 of RWT in WAIM. The values of A11 were predicted by the trained neural network by changing two process parameters, while keeping the others constant. The 3D response surfaces and contour plots were used to describe the interaction effect of process parameters on A11.As shown in Figures7 and 8, the value of A11 decreases as the melt temperature rises. The reasons come from two aspects. Firstly, the increasing melt temperature results in a decrease in viscosity. At the same shear rate, the shear stress decreases, and this is not conducive to the fiber orientation. Secondly, during the melt filling stage, the polymer molecular chains are also aligned, and the high melt temperature facilitates the deorientation behavior of the molecular chains after the completion of water injection process, which changes the orientation distribution of the short fibers and decreases A11.Figure 7
3D response surface and contour plot of interaction effect of melt temperature and water pressure on A11.Figure 8
3D response surface and contour plot of interaction effect of melt temperature and water temperature on A11.The values of A11 decrease with the increase in the water pressure (Figures 7 and 9). After melt filling stage, the short fibers in the shell layer highly orient along the melt flow direction. During the high-pressure water filling stage, the inner layers of RWT bear the pressing and friction of water column as well as the stretching of the moving melt. The short fibers originally oriented along the melt flow direction are posture-adjusted and arranged in a disordered state. The greater the water pressure is, the stronger the disturbance to the RWT is, and the smaller A11 in the melt flow direction is.Figure 9
3D response surface and contour plot of interaction effect of water pressure and water temperature on A11.While the water injection temperature rises, the values of A11 of RWT in WAIM increase (Figures 8 and 9). The polymer melt has a viscoelastic property and can return to the original state after the external force is removed. The multiple disturbances cause the short fibers to be disordered in the inner layers of RWT during the water filling stage, and the higher water temperature favors the elastic recovery of the short fiber posture, thereby resulting in an increase of A11.


## 4. Conclusions

The iARD-RPR model was used to simulate the orientation of short fibers in WAIM. Compared with the SEM micrographs, the simulation results indicated that this model was suitable for the SFO prediction in overflow WAIM. The short fibers in the region near the mold cavity mostly oriented in the melt flow direction, while those in the region near the water channel were disordered. The melt temperature, water pressure, and water temperature have significant effects on the SFO along the melt flow direction. It was found that the values of A11 decreased with increasing water injection pressure and melt temperature and increased with increasing water temperature. Finally, this study is beneficial for the subsequent optimization of SFO in WAIM.



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*Source: 7201215-2019-08-19.xml*