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

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 and A11 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.


Introduction
With the development of advanced, economical, and environmentally friendly society, higher requirements are placed on the performance of plastic products. e 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]. e 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]. e 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][15][16].
e 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. e process parameters can be accurately controlled in simulation, but the reliability of SFO simulation depends on the accuracy of the mathematical model. e 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. e 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. e 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.

Fluid Mechanics Governing Equations.
e 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. e governing equations for transient and nonisothermal fluid motion in WAIM processes are as follows: 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, C p is the specific heat capacity, T is the temperature, k is the thermal conductivity, t is the time, and _ c is the shear rate.

Rheological Model.
e 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].
where η is the viscosity of the melt, η 0 is the zero shear viscosity, _ c is the shear rate, τ * is the material constant, n is the power law index in the shear rate, T is the melt temperature, T c is the glass transition temperature, and D 1 , D 2 , D 3 , A 1 , and A 2 are the constants associated with the selected material.

Fiber Orientation Model.
e fiber orientation is often described by two methods including orientation probability density distribution function and orientation tensor. e 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]: where ψ(P) 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. e three components A 11 , A 22 , and A 33 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: 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 C I (0 < C I < 0.1) and fiber-matrix interaction influence factor C M (0 < C M < 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. e orientation tensor will be influenced by the parameters, and the default parameters (C I � 0.005, C M � 0.1, and α � 0.7) of iARD-RPR model for short fibers were used in the simulations: where W is the vortex tensor, D is the deformation rate tensor, ξ is the particle shape factor, and the fourth-order orientation tensor A 4 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.
where _ Λ IOK is a material derivative of a diagonal tensor and its superscript is the intrinsic orientation kinetic (IOK) is a rotation matrix, λ i is the eigenvalue of matrix A, and In the simulation, the melt flow problem was solved firstly, and then the resulting velocity field was applied to compute the short fiber orientation.

Geometric Model.
e geometric model is shown in Figure 1. e length of the functional hollow duct is 245 mm, and the diameter is 20 mm. e overflow cavity was used to accommodate the melt pushed out by the high-pressure water. e connecting pipe between the duct and the overflow cavity had a diameter of 10 mm. e 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. e combined model, imported into the Moldex3D R15.0, was meshed with Boundary Layer Mesh format. In the simulation, a short-fiberreinforced 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. e 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.

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. e 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). ese factors and their corresponding levels are listed in Table 1.
e full factorial experimental design method demands 5 6 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 (L 25 (5 6 )) was used to arrange the test plan. In the final stage, the short fibers were mainly oriented along the melt flow direction. e 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 A 11 is the average value of these points ( Table 2).
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. e factor with the maximum range is regarded as the most sensitive process parameter.

Advances in Materials Science and Engineering
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, K ij is the sum value of A 11 of all level in each factor, y ijk is the value of the k-th experimental result, p 1 is the test times with the j-th factor and the i-th level, and range R j represents the influence degree of the factor. e larger the range R j is, the stronger the influence of the factor is.

Analysis of Variance.
Analysis of variance was employed to determine which factors were significant. In this method, the F value of each factor is used to indicate the degree of influence.
where T is the sum of test indexes, n is the number of levels, m is the number of factors, S sum is the total differences, S j is the differences of test results caused by the change in every level of factor j, S e is the differences of the test caused by the error, p 0 is the total test times, f j and f e are the degrees of   Given a significant level α, the criterion F α (f j , f e ) can be found in the F distribution table. e influence of a factor with F value greater than F α value is significant. 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. e 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.

Artificial Neural
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. e 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).

Comparison between Simulation Result and Experimental
Observation. Figure 3 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. e values of A 11 first increase and then decrease from the region near the mold cavity to the region near the water channel, and the larger values of A 11 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 A 11 and disordered region with small A 11 . To verify the simulation results, water-assisted injection molded tubes with shortfiber-reinforced composite were observed by scanning electron microscopy (Nova NanoSEM 450) with an accelerating voltage of 5 kV. e tubes were produced by a laboratory-developed WAIM device improved from a 110ton traditional injection molding machine. e material was PP containing 30% short grass fibers. e 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. e surface of the sample was subjected to goldsputtered 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. e experimental observations are consistent with the simulation results, indicating that the iARD-RPR model can well predict the SFO distribution in overflow WAIM. Figure 5 shows the values of A 11 obtained from 100 points along the diameter in the simulation. e melt filling process was similar to the conventional injection molding process, in which the short fibers oriented with a typical shell-core structure. e 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. e values of A 11 increased from about 0.7 to 0.88 within the thickness of 0 to 2 mm. is could be Advances in Materials Science and Engineering 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 A 11 increases.

Analysis of Short Fiber Orientation Mechanism in WAIM.
In the water filling stage, the values of A 11 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 A 11 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. e 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.

Sensitivity Analysis.
e results of range analysis are shown in Table 3. e rank of sensitivity of A 11 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 A 11 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 A 11 . e filling rate, mold temperature, and delay time are not significant for A 11 with the F values of 0.924, 1.163, and 0.913, respectively. (Table 2), 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. e trained neural network was used to predict A 11 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  Advances in Materials Science and Engineering relationship between the process parameters and A 11 and could provide acceptable predictions.

Interaction Effect of Significant Process Parameter.
Range and variance analysis results show that melt temperature, water pressure, and water temperature are significant for A 11 of RWT in WAIM. e values of A 11 were predicted by the trained neural network by changing two process parameters, while keeping the others constant. e 3D response surfaces and contour plots were used to describe the interaction effect of process parameters on A 11 .
As shown in Figures 7 and 8, the value of A 11 decreases as the melt temperature rises. e 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 A 11 . e values of A 11 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. e short fibers originally oriented along the melt flow direction are posture-adjusted and arranged in a disordered state. e greater the water pressure is, the stronger the disturbance to the RWT is, and the smaller A 11 in the melt flow direction is.   While the water injection temperature rises, the values of A 11 of RWT in WAIM increase (Figures 8 and 9). e polymer melt has a viscoelastic property and can return to the original state after the external force is removed. e 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 A 11 .

Conclusions
e 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. e 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. e 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 A 11 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.

Data Availability
e data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest
e authors declare that there are no conflicts of interest regarding the publication of this paper.