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Optimization and numerical simulation analysis for molded thin-walled parts fabricated using wood-filled polypropylene composites via plastic injection molding.

INTRODUCTION

Injection molding in serial production is mostly a very stable process. It is discontinues and complicated process, which involves the interaction of several variables for the achievement of a good-quality part. These variables can be classified in terms of molding parameters, materials, product design, and mold design. The process needs appropriate setting parameters. The selection of appropriate machining parameters for the injection-molding process becomes more difficult for applications that concern thin-walled parts and use lignocellulosic polymer composites (i.e., wood-filled polypropylene composites). Furthermore, the processing of lignocellulosic polymer composites is usually limited to temperatures below 230[degrees]C to minimize fiber degradation as reported by Sanadi et al. [1]. Processing at low temperatures makes it difficult for the polymer melt to flow into the mold cavities and often leads to an inconsistent distribution of residual stresses, volumetric shrinkage and warpage in molded products, particularly in thin-walled parts.

Residual stresses, shrinkage, and warpage are the three major challenges in injection molding. They are inevitable in many cases, especially for parts with complex geometries, thin-walled parts, micro-parts, certain materials, etc. According to Jacques [2], the warpage of a molded part results from an asymmetrical stress distribution over the thickness of the part that is caused by unbalanced cooling during the cooling stage but also by anisotropic fiber or filler orientations. Therefore, the thinnest region is the most sensitive to warpage. Furthermore, Wang et al. [3] have also noted that residual stresses in turn result from nonuniform shrinkage of molded parts. Unfortunately, a nonuniform shrinkage distribution on molded parts is inevitable because of several factors related to inconsistent cooling processes, nonuniform pressure distributions, the orientation of molecules and/or additives, part design, mold design, processing conditions, and the interactions among all these factors.

Therefore, a researcher or process engineer should have knowledge and understanding of methods for the statistical design of experiments (DOE) to identify the optimum interactions among the variables in the injection-molding process. Previously, Azaman et al. [4] have reported that it is preferable to design a product that uses shallow thin-walled parts rather than fiat thin-walled parts when molding lignocellulosic polymer composites in terms of quality, rigidity of structure, and economy. On the basis of this premise, the next stage of optimization concerns the selection of variables in the molding process parameters (filling time, packing pressure, packing time, cooling time, mold temperature, injection pressure, etc.) and the determination of their effects on part quality. According to Giboz et al. [5], the level of warpage and shrinkage is highly dependent on the molding process parameters. They propose that efforts to reduce warpage and shrinkage to an acceptable level should be focused on the careful control of the molding process parameters.

Because of these considerations, application of the Taguchi method, the signal-to-noise and analysis of variance (ANOVA) seems to be a more practical approach to the statistical design of experiments than other methods, which appear to be more complicated. The more complex approaches of other researchers [6, 7], such as Liao and Hsieh [6], who have applied back-propagation artificial neural networks (BPANN) to this problem, do yield more optimal process conditions than does Taguchi's method of minimizing the shrinkage and warpage of injected thin-walled parts. However, in the author's view, the selection of a method for the statistical design of experiments should be based on multiple criteria, including practicality, efficacy, ease of construction, and adequate accuracy. These criteria, when considered collectively, favor the Taguchi method.

Many industries concur with this assessment and have employed the Taguchi method over the years to improve their products or manufacturing processes. The Taguchi approach seems to provide practical and effective tools for solving challenging quality problems. This method has been used quite successfully in several industrial applications such as the optimization of manufacturing processes and the design [3, 8]. According to Chin [9], the Taguchi method was developed by systematically allocating factors and levels to suitable orthogonal arrays, then performing an analysis of the signal-to-noise ratio (S/N) and an analysis of variance (ANOVA) to determine the optimal combination of parameters, validate the results and identify the significant parameters that affect the part quality. This article presents a detailed method for such a calculation that can be used as a reference for researchers/engineers to build their calculations using available software, such as Microsoft Office Excel.

Chen et al. [10] have analyzed a cover component used in a power supply that was molded using a novel heat-resistant polyamide (PA9T). The application of numerical simulations (MoldFlow) and the integration of experimental and statistical techniques were used to determine the optimal parameter selection and the most significant parameters that affected the warpage results. The melt temperature and the packing pressure were found to be the most significant factors that affected warpage in both the simulation and the experimental analysis.

Altan and Yurci [11] have focused on the optimization of the injection-molding process parameters (melt temperature, mold temperature, and cooling time) to minimize thermal residual stresses in the surface regions of polystyrene (PS) and high-density polyethylene (HDPE) parts. The melt temperature and mold temperature were found to be the most significant parameters that affected residual stresses in the surface regions of the PS and HDPE parts, respectively. Altan [12] has also investigated the optimal molding parameters to ensure the minimum shrinkage value for a rectangular-shaped molded part using the Taguchi method and ANOVA. The results demonstrated that the packing pressure and melting temperature were most significant factors that affected the shrinkage of the polypropylene (PP) and polystyrene (PS), respectively, while the injection pressure was the least significant factor for both materials.

Wang et al. [3] have investigated the effects of processing parameters, including melt temperature, injection time, packing pressure, packing time, and cooling time, on the warpage problem for a molded LCD TV front shell. The results indicated that the part warpage can be effectively reduced based on the optimal combination of parameters determined using signal-to-noise analysis, while the packing pressure is the most significant factor in controlling the warpage. Chang and Faison [13] have studied the shrinkage and optimization of general-purpose polystyrene (GPS), high-density polyethylene (HDPE), and acrylonitrile-butadiene-styrene (ABS) parts using the Taguchi method and ANOVA. The optimization results indicated that the mold and melt temperatures as well as the holding pressure and holding time were the most significant factors that affected the shrinkage.

Erzurumlu and Ozcelik [14] have constructed a study applying three-level factorial designs, an L18 orthogonal array, the S/N ratio, ANOVA and numerical simulation to determine the effects of rib design and molding parameters on warpage and sink indices. The packing pressure was found to be the most significant factor affecting warpage and sink indices in PC/ABS, while the melting temperature and rib layout angle had significant effects on the warpage and sink index, respectively, for polyamide (PA66) material. Ozcelik and Sonat [15] via ANOVA analysis, have shown that the packing pressure has the greatest effect on the warpage of the final product and that the casing structure is strongest when filled with 15 wt% carbon fiber-reinforced PC/ABS, compared with casing with no filler.

Huang and Tai [16] have used computer simulations (C-MOLD[TM]) with the assistance of the Taguchi method to determine the effective molding parameters that affect the warpage behavior of a molded, box-shaped geometric part. The results indicated that packing pressure is the most important factor that affects warpage. Of the other factors, gate dimension and filling time were found to be the least important factors affecting warpage. In addition, Wang et al. [17] have also reported that packing pressure is the most important factor that affects the shrinkage and warpage of injected thin-walled parts. Tang et al. [18] have conducted experiments based on the Taguchi method, using an L9 orthogonal array and optimizing via ANOVA. This research investigated the effects of molding parameters such as melt temperature, filling time, packing pressure and packing time on the warpage of thin-walled parts. The results indicated that melting temperature was the most significant factor, while filling time was least significant factor that affected the minimum warpage.

Oktem et al. [19] have used the Taguchi method to investigate the warpage problem related to shrinkage variation, which can be reduced by modulating the molding parameters (injection time, packing pressure, packing time, and cooling time) for molded thin-shell features. The results indicated that the packing pressure and the packing time were the most significant factors affecting the warpage and shrinkage behaviors. Lio et al. [20] using the Taguchi method and an F-test, have found that the packing pressure is the most important factor that affects the warpage and shrinkage of a molded thin-walled part, while other parameters such as mold temperature, melt temperature, and injection speed were less significant.

The limitation of all this research is the lack of direct experimental validation of the results of numerical simulations. However, previous researchers have nevertheless used results from numerical simulations for the purpose of optimization. Hakimian and Sulong [8] have also used numerical simulations to determine the optimal molding parameters for molded microgears fabricated using polymer composites. A similar approach has been used by Erzurumlu and Ozcelik [14] to investigate the minimization of the warpage and sink index of molded thermoplastic parts.

A review of previous studies indicates that investigations of molded thin-walled parts fabricated using lignocellulosic polymer composites in the injection-molding process have been rarely reported. Most researchers have focused on the use of nonreinforced polymers and reinforced polymers in the molding of thin-walled parts to investigate the relations between the molding parameters and their effects on part quality. Hence, the aim of this study is to investigate the effects of injection-molding parameters during the post-filling stage (packing pressure, packing time, mold temperature, and cooling time) and to determine the optimal selection of parameters for three different types of lignocellulosic polymer composite materials with respect to in-cavity residual stresses, volumetric shrinkage, and warpage properties. Numerical simulations with the assistance of the Taguchi method, the S/N ratio and ANOVA are used in this research. In addition, this study should permit the reduction of trial molding times and the improvement of part quality, and importantly, it can also serve as a reference in the further investigation of the molding defects of thin-walled parts fabricated using lignocellulosic polymer composite.

METHODOLOGY

Part Design

Autodesk Inventor Professional[R] was used to model the thin-walled molded parts, as shown in Fig. 1. A shallow, thin-walled part was created as a 3D design. The general dimensions of the part were 55 mm x 50 mm x 0.7 mm.

Numerical Simulation

Autodesk MoldFlow Insight[R] was used to simulate and analyze the injection-molding process. A mesh model was developed, as shown in Fig. 2. The injection-molding machine and the materials used in the simulation adhered to the following specifications: Arburg Allrounder 370c 88-ton injection-molding machine (screw diameter of 30 mm); PP + 40 wt% wood composite from NCell 40, GreenCore Composites; PP + 50 wt% wood composite from Isoform Lip CPCW50, Isokon; and PP + 60 wt% wood composite from WPC-2-mv, Fraunhofer Institut. Table 1 shows the specifications of the injection-molding machine. Table 2 shows the material properties of the lignocellulosic polymer composites. The simulation was performed using a set analysis (Fill + Cool + Fill + Pack + Warp) for these models. The post-filling processing parameters for the simulations are shown in Table 3. The fixed parameters used for further detailed analysis of the post-filling parameters are setting the injection time to 1 s; melt temperature to 185[degrees]C; ejection temperature to 104[degrees]C and mold open time to 5 s.

Measurement of In-Cavity Residual Stresses, Volumetric Shrinkage, and Warpage

The results indicate that the in-cavity residual stresses lie along the first principle direction (plotted at the centre of the surface). According to the suggestion of Altan and Yurci [11], the stress values near the surface region were taken into account during the optimization analysis. High stresses near the surface regions of molded parts cause the parts to be more susceptible to cracking caused by environmental stresses and to be sensitive to chemical diffusion. Similarly, the volumetric shrinkage and warpage were measured at the center of the surface.

Experimental Design

Structure of Taguchi Orthogonal Arrays. Taguchi orthogonal arrays consisting of 16 experiments with four levels were applied, as four processing parameters were used in the analysis. Table 4 lists the processing parameters and levels, while Table 5 shows the detailed arrangement of the orthogonal arrays that was used for the analysis of the three investigated materials.

Statistical Analysis. In determination of the signal to noise (S/N), the smaller-is-better quality characteristic has been selected for each of three responses: in-cavity residual stresses, volumetric shrinkage, and warpage. The optimum set of parameters was determined from the S/N ratio for each polymer composite. The penultimate stage of the Taguchi method is the verification of the predicted results via confirmation on the optimum set of parameters. In addition, the collected data were also analyzed using ANOVA to determine which significant factors would affect all responses. An example of a statistical calculation is presented in Appendix A.

RESULTS AND DISCUSSION

Analysis of the S/N Ratio For Optimization of Molding Parameters and Materials

The average results for in-cavity residual stresses, volumetric shrinkage and warpage for different types of lignocellulosic polymer composites (PP + 40 wt% wood, PP + 50 wt% wood and PP + 60 wt% wood) were determined and are shown in Table 6. In the Taguchi method, the (S/N) ratio is used to analyze the collected data to determine the optimal combination of parameters and also to predict the optimum value. A smaller-is-better S/N ratio was defined following Eq. 1 in Appendix A. This selection is appropriate to the study's objectives regarding the reduction of residual stresses, volumetric shrinkage and warpage on molded thin-walled parts using different types of lignocellulosic polymer composites.

Based on Table 7, the highest S/N ratio for each processing parameter gives the optimum combination of processing conditions for the minimization of the three selected responses of the investigated materials, while the difference between the maximum and minimum values of the S/N ratio for each mean parameter can be used to make an initial prediction regarding the parameters that significantly influence the responses. This claim is proven and explained by a statistical analysis using analysis of variance (ANOVA) in a discussion that appears later in the paper. The study found that the packing pressure and mold temperature are the most important parameters for the reduction of residual stresses and volumetric shrinkage on thin-walled parts. This is contrary to the results for the reduction of warpage, for which the most important processing parameters for all three materials are the packing pressure, packing time and cooling time.

As listed in Table 8, for the PP + 40 wt% wood material, the optimum processing conditions for the minimization of in-cavity residual stresses, volumetric shrinkage and warpage are a packing pressure of 0.95[P.sub.inject], a packing time of 30 s, a mold temperature of 40[degrees]C and a cooling time of 40 s; a packing pressure of 0.95[P.sub.inject], a packing time of 40 s, a mold temperature of 40[degrees]C and a cooling time of 10 s; and a packing pressure of 0.85[P.sub.inject], a packing time of 40 s, a mold temperature of 50[degrees]C and a cooling time of 10 s, respectively.

For PP + 50 wt% wood, the optimum processing conditions for the minimization of in-cavity residual stresses, volumetric shrinkage, and warpage are a packing pressure of 0.95[P.sub.inject], a packing time of 10 s, a mold temperature of 40[degrees]C and a cooling time of 30 s; a packing pressure of 0.95[P.sub.inject], a packing time of 40 s, a mold temperature of 40[degrees]C and a cooling time of 30 s; and a packing pressure of 0.95[P.sub.inject], a packing time of 10 s, a mold temperature of 55[degrees]C and a cooling time of 40 s, respectively.

For PP + 60 wt% wood, the optimum processing conditions for the minimization of in-cavity residual stresses, volumetric shrinkage and warpage are a packing pressure of 0.95[P.sub.inject], a packing time of 10 s, a mold temperature of 45[degrees]C and a cooling time of 30 s; a packing pressure of 0.95[P.sub.inject], a packing time of 10 s, a mold temperature of 45[degrees]C and a cooling time of 30 s; and a packing pressure of 0.95[P.sub.inject], a packing time of 10 s, a mold temperature of 50[degrees]C and a cooling time of 30 s, respectively.

The optimum combination of parameters for each material to affect each response (in-cavity residual stresses, volumetric shrinkage, and warpage) were varied, although the values used for the processing parameters were the same, as shown in Table 3. This difference can be attributed to the different filler loadings and also to the different viscosity behaviors of each lignocellulosic polymer composite, as shown in Table 2. These differences affect the flow behavior along thin-walled parts.

Simulation Verification Run

By substituting the optimum S/N ratio into Eq. 30 for the optimum combination of processing parameters, the predicted minimum responses for the three lignocellulosic polymer composites were obtained. All values are given in Table 8. However, it was necessary to perform verification tests to confirm the efficacy of the optimal parameter combinations in producing the desired responses. The results demonstrated that the simulated results agreed well with the predicted results, and most relative deviation errors were less than 10%. However, relative deviation errors of more than 10% were obtained for PP + 60 wt% wood. This is because the short-shot problem occurred in these molded thin-walled parts. This problem caused the data obtained to be unable to provide precise values for the determination of the S/N ratio values compared to the other types of lignocellulosic polymer composites. Although the melt flow rate was in the appropriate range (greater than 3.0 g/10 min) for the injection-molding process, the conditions were not suitable for molded thin-walled parts. The high filler content made it difficult for the melt to flow easily, and rapid solidification occurred. This finding conflicts with some statements made by Hakimian and Sulong [8]; they have claimed that the short-shot problem occurs during the molding of microparts because of insufficient injection speeds, injection pressure, and molding temperatures. Hence, a material with a wood-filler content of greater than 50 wt% is not suitable for molding thin-walled parts using lignocellulosic-filler-reinforced thermoplastic composites.

From the optimum processing conditions setting for the minimization of volumetric shrinkage, the Fig. 3 illustrates the overpacking that occurred in the gate regions for all three lignocellulosic polymer composites. However, the values of overpacking were in the ranges of -1.980% to -1.025% and -1.182% to -0.115% for PP + 40 wt% wood and PP + 60 wt% wood, respectively, while the value for PP + 50 wt% wood was in the ranges of -0.709% to -0.174%. These value differences can be explained in terms of the viscosity and shear rate behaviors rather than the filler contents. The melt flow index for PP + 50 wt% wood was higher than the others, as shown in Table 2. A high value of the melt flow index indicates low-viscosity behavior.

In addition, Fig. 4 shown the viscosity behavior for PP + 40 wt% wood shows Newtonian flow at low shear rates and power law behavior until high shear rates whereas the PP + 50 wt% and PP + 60 wt% wood show nearly ideal power law behavior within the measured range of shear rates. Although the viscosity behavior of PP + 60 wt% wood is approximately similar to PP + 50 wt% wood, the higher amount of filler increases the viscosity by approximately a factor of 5 and becomes the limiting factor in molding. The low viscosity at high shear rates permits the melt to flow more easily when molding thin-walled parts. This is caused by orientation of the long polymer chains, a phenomenon often referred to as shear thinning. This behavior allows processors to move polymer composite melts long distances through thin-walled part and minimizes the occurrence of overpacking in various regions of the molded parts. Therefore, the differences in volumetric shrinkage on the part surface become smaller and more uniformly distributed.

Based on the predicted results, the in-cavity residual stresses for PP + 50 wt% wood should be 20.06 MPa, which is lower than the 20.63 MPa and 31.12 MPa stresses expected for PP + 40 wt% wood and PP + 60 wt% wood, respectively. This may be attributable to the homogeneity of the filler-to-matrix-polymer ratio, which causes the thermal stresses to be uniformly distributed along thin-walled parts. Meanwhile, the predicted volumetric-shrinkage value for PP + 50 wt% wood is 1.62%, which is higher than the values of 0.58% and 0.99% predicted for PP + 40 wt% wood and PP + 60 wt% wood, respectively. However, visual inspection of Fig. 3 shows that the differences in the values of the contour-pattern distribution for PP + 50 wt% wood are small compared with the other types of lignocellulosic polymer composites. Smaller variations in value in the contour-pattern distribution indicate a better distribution of the volumetric shrinkage on the part surface. A molded part may become distorted because of a lack of homogeneity in its shrinkage. A negative value of volumetric shrinkage in the contour pattern indicates that overpacking occurred. Greater overpacking was observed for the PP + 40 wt% wood polymer composite than for PP + 50 wt% wood. Any non-homogeneity in volumetric shrinkage will also affect the warpage results. Therefore, PP + 50 wt% wood is considered to be suitable for applications that involve molding thin-walled parts using lignocellulosic polymer composites.

Based on the obtained predictions, the expected warpage is ~0.01 mm for all types of lignocellulosic polymer composites. The value calculated for PP + 40 wt% wood is 0.0064 mm, which is lower than the values of 0.0150 mm and 0.0156 mm calculated for PP + 50 wt% wood and PP + 60 wt% wood, respectively. However, among the three investigated materials, PP + 50 wt% wood is the preferred type of lignocellulosic polymer composite for the manufacture of molded thin-walled parts. This material, which was selected after evaluation in terms of economy in manufacturing, offers good results with respect to the quality of the final part without significant risk of the short-shot problem and represents the optimal or maximum filler content that should be used in a polymer composite.

Analysis of Variance (ANOVA)

Tables 9-11 show the results of the ANOVA for in-cavity residual stresses, volumetric shrinkage, and warpage determined for thin-walled parts fabricated using the three investigated types of lignocellulosic polymer composites. The following discussion focuses on the ANOVA results obtained for the thin-walled parts molded using PP + 40 wt% wood and PP + 50 wt% wood. The ANOVA results for PP + 60 wt% wood are neglected. This material was found to be unsuitable for molded thin-walled parts because of the problem of early solidification (short-shot problem), and the analysis yielded statistical residual errors that were higher than the molding parameters. Wang et al. [3] have also reported similar results concerning the percentage contributions to the residual errors. This can most likely be attributed to some interaction between parameters that was not included in the statistical design of the experimental set-up.

On the basis of the ANOVA results summarised in Table 9, the packing pressure and mold temperature are the most significant factors that affect the in-cavity residual stresses for molded parts fabricated using PP + 40 wt% wood and PP + 50 wt% wood, respectively. The packing pressure serves to ensure the replenishment of polymer melt into the molded part, which is necessary because of the loss caused by the volume shrinkage of the cooled part during the solidification phase. Zhou and Li [21] have noted that during the packing stage, the frozen-in stress caused by the packing pressure should be taken into account when measuring residual stresses. Wang and Young [22] have reported that the effect of packing pressure on residual stresses is usually more significant at low packing pressures than at high packing pressures, resulting in a nearly identical distribution of residual stresses. In contrast to the results of Azaman et al. [23], a lower packing pressure can lead to early solidification phenomena during the packing stage, causing the inner stress on the part to increase. The optimal results also indicate that the optimum packing pressures for all three types of lignocellulosic polymer composites are almost all at the same high level, as shown in Table 8. In contrast, Altan and Yurci [11] have indicated that the most important parameter for the distribution of residual stresses on the surface regions of high-density polyethylene (HDPE) parts is the mold temperature. The mold temperature was determined to be the most effective parameter for the reduction of residual stresses because when the mold is hotter, the cooling rate is slower [24]. As a consequence, a higher mold temperature is able to increase the crystallization time, which consequently induces slower crystallization with lower stresses that become frozen with sufficient relaxation.

The results summarized in Table 10 show that the packing pressure is the most significant parameter that contributes to the volumetric shrinkage for PP + 40 wt% wood, while the packing pressure and mold temperature are the most effective parameters for the control of the volumetric shrinkage of molded parts fabricated using PP + 50 wt% wood. Similarly, Lotti et al. [25] have found that the holding pressure and mold temperature most influence the distribution of shrinkage along molded parts. It has been shown that the volumetric shrinkage is more strongly dependent on the packing pressure and mold temperature for molded thin-walled parts that are fabricated using lignocellulosic-filler-reinforced thermoplastic composites. The packing pressure assists in filling additional polymer melt into the mold for compensation during the solidification phase. Meanwhile, the mold temperature helps to ensure consistent solidification rates during the solidification process between the cavity and the core side of the mold for thin-walled parts. Bushko and Stokes [26] have reported that the packing pressure has a significant effect on shrinkage. As expected, a higher packing pressure results in lower shrinkage in both the in-plane and through-thickness directions. A low packing pressure results in high volumetric shrinkage. Thus, the magnitude of the packing pressure has an effect on the shrinkage distribution of the molded part. According to Azaman et al. [27], an optimum mold temperature in the range of 40-45[degrees]C provides an adequately low orientation between the fillers and the matrix polymer, resulting in the minimal volumetric shrinkage for a molded thin-walled part. However, Jansen et al. [28] have found the mold temperature does not have a great effect on shrinkage for molded parts fabricated using semi-crystalline materials that are unfilled and or filled with glass fibers.

For PP + 40 wt% wood, the most significant factor that affects warpage is the packing time (49.19%), and the packing pressure (29.69%) is the second-most effective parameter, as shown in Table 11. Meanwhile, for PP + 50 wt% wood, the most significant factor that affects the warpage of a molded thin-walled part is the packing pressure (35.20%), followed by the cooling time (11.24%). Several studies have reported a relation between packing time and packing pressure. If the time of compaction is too long, excess polymer melt will be packed into the cavity during the packing stage. Inconsistency in the compressive stress between the frozen layers of the part surface and the mold wall causes increased warpage [18, 29, 30]. Azaman et al. [27] have found that further increasing the cooling time does not significantly affect the distribution of warpage on a molded thin-walled part. However, their findings agree that the cooling time plays an important role in providing more time for the relaxation of the orientation between the filler and the matrix polymer during the cooling stage. In contrast, Zhou and Li [30] have reported that a longer cooling time can lead to a significant decrease in warpage because of the decreased effects of thermal strain and deformation after demolding.

CONCLUSIONS

In conclusion, Taguchi optimization is sufficient to optimise with respect to in-cavity residual stresses, volumetric shrinkage and warpage for molded thin-walled parts fabricated using lignocellulosic polymer composites. Of the lignocellulosic polymer composites investigated in this study, the PP + 50 wt% wood material is preferred over PP + 40 wt% wood and PP + 60 wt% wood for the manufacture of molded thin-walled parts. This determination was based on the following considerations: (1) The predicted in-cavity residual stresses for PP + 50 wt% wood are ~20.10 MPa, which is lower than the values of ~2 0.60 MPa and 31.10 MPa predicted for PP + 40 wt% wood and PP + 60 wt% wood, respectively. (2) The differences in value of the contour-pattern distribution for PP + 50 wt% wood are small compared to those for the other types of lignocellulosic polymer composites. (3) The predicted warpage is ~0.01 mm for all types of lignocellulosic polymer composites. This material, which was selected after evaluation in terms of economy in manufacturing, yields good results in terms of part quality without a significant risk of encountering the short-shot problem and represents the optimal or maximum filler content that should be used in preparing polymer composites. The ANOVA analysis found that the packing pressure and mold temperature are the most important parameters for the reduction of residual stresses and volumetric shrinkage, while the packing pressure, packing time, and cooling time are significant to the reduction of warpage in molded thin-walled parts fabricated using various types of lignocellulosic polymer composites.

APPENDIX A:

Step 1: Selection of factors

                                          PP + 50 wt% wood
                                               Levels

Factor   Description (unit)               1    2    3    4

A        Packing pressure (%)             80   85   90   95
B        Packing time (sec)               10   20   30   40
C        Mold temperature ([degrees]C)    40   45   50   55
D        Cooling time (sec)               10   20   30   40

Step 2: Orthogonal array

The [L.sub.16][4.sup.4] orthogonal array of the Taguchi method was
selected to correspond to the experimental setup.

             Parameters

No. trial   A   B   C   D

1           1   1   1   1
2           1   2   2   2
3           1   3   3   3
4           1   4   4   4
5           2   1   2   3
6           2   2   1   4
7           2   3   4   1
8           2   4   3   2
9           3   1   3   4
10          3   2   4   3
11          3   3   1   2
12          3   4   2   1
13          4   1   4   2
14          4   2   3   1
15          4   3   2   4
16          4   4   1   3


Step 3: The combination of parameters on the orthogonal [L.sub.16][4.sup.4] array

The parameters for the effective factors are arranged on the orthogonal array shown at Table 5 at page 5.

Step 4: Experimental objective

The research objective is to determine the optimum parameters that minimize three responses: in-cavity residual stresses, volumetric shrinkage and warpage.

Step 5: Quality characteristics

In determining S/N ratio, the smaller is the better quality characteristic was selected

S/N = -10log [1/n][n.summation over (i=1)] [yi.sup.2] (A1)

Remarks: yi is the value of volumetric shrinkage for the ith test; n is the number of tests.

The following is an example of the calculation using the volumetric-shrinkage result for PP + 50 wt% wood, taken from Table 6:

S/N = -10log [1/1][1.summation over (i=1)] [3.181.sup.2] = -10.05 dB (A2)

Then, the average S/N ratio can be determined:

[bar.S]/N = (-10.05 - 11.03 - 8.06 ... -5.79 - 4.65)/16 = -7.73 dB (A3)

Step 6: Determine the effects of the average S/N ratios of factors at different levels

For packing pressure:

Level 1 = (-10.05 - 11.03 - 8.06 - 9.35)/4 = -9.62 dB (A4)

Level 2 = (-6.76 - 6.11 - 9.61 - 8.22)/4 = -7.68 dB (A5)

Level 3 = (-7.58 - 9.20 - 5.42 - 6.60)/4 = -7.20 dB (A6)

Level 4 = (-9.25 - 6.06 - 5.79 - 4.65)/4 = -6.64 dB (A7)

For packing time:

Level 1 = (-10.05 - 6.76 - 7.58 - 9.25)/4 = -8.41 dB (A8)

Level 2 = (-11.03 - 6.11 - 9.20 - 6.06)/4 = -8.10 dB (A9)

Level 3 = (-8.06 - 9.61 - 5.42 - 5.79)/4 = -7.22 dB (A10)

Level 4 = (-9.35 - 8.22 - 6.60 - 4.65)/4 = -7.20 dB (A11)

For mold temperature:

Level 1 = (-10.05 - 6.11 - 5.42 - 4.65)/4 = -6.56 dB (A12)

Level 2 = (-11.03 - 6.76 - 6.60 - 5.79)/4 = -7.54 dB (A13)

Level 3 = (-8.06 - 8.22 - 7.58 - 6.06)/4 = -7.48 dB (A14)

Level 4 = (-9.35 - 9.61 - 9.20 - 9.25)/4 = -9.35 dB (A15)

For cooling time:

Level 1 = (-10.05 - 9.61 - 6.60 - 6.06)/4 = -8.08 dB (A16)

Level 2 = (-11.03 - 8.22 - 5.42 - 9.25)/4 = -8.48 dB (A17)

Level 3 = (-8.06 - 6.76 - 9.20 - 4.65)/4 = -7.17 dB (A18)

Level 4= (-9.35-6.11-7.58-5.79)/4 = -7.21 dB (A19)

Here, the average S/N ratios for the volumetric shrinkage of PP + 50 wt% wood polymer composites are summarised.

Factor   Level 1   Level 2   Level 3   Level 4

A        -9.62      -7.68    -7.20     -6.44 *
B        -8.41      -8.10    -7.22     -7.20 *
C        -6.56 *    -7.54    -7.48     -9.35
D        -8.08      -8.48    -7.17 *   -7.21


Remarks: * indicates the factor level at the optimal condition or the highest value.

Step 7: Identify the best combination of parameters to minimize volumetric shrinkage

Factor             Level    Parameter Value

Packing pressure     4     0.95[P.sub.inject]
Packing time         4           40 sec
Mold temperature     1        40[degrees]C
Cooling time         3           30 sec


Step 8: Estimate the optimum value determined using the best combination of parameters

i) Determine the overall average S/N ratio

[bar.T] = [SIGMA][[F.sub.A,L1,L2,L3,L4] + [F.sub.B,L1,L2,L3,L4] + [F.sub.C,L1,L2,L3,L4] + [F.sub.D,L1,L2,L3,L4]]/m (A20)

Remarks: m is the total number of average factor effects.

[bar.T] = [(-9.62 - 7.68 - 7.20 - 6.44) + ... + (-8.08 - 8.48 - 7.17 - 7.21)]/16 (A21)

= - 7.73 dB (A22)

ii) Determine the total contribution

[bar.C] = ([F.sup.*.sub.A]-[bar.T]) + ([F.sup.*.sub.B]-[bar.T]) + ([F.sup.*.sub.C]-[bar.T]) + ([F.sup.*.sub.D]-[bar.T]) (A23)

Remarks: * indicates the factor level at the optimum condition

[bar.C] = (-6.44 + 7.73) + (-7.20 + 7.73) + (-6.56 + 7.73) + (-7.17+7.73) (A24)

= 3.57 dB (A25)

iii) Calculate the predicted optimum value

[bar.O] = [bar.T] + [bar.C] (A26)

[bar.O] = -7.73 + 3.57 (A27)

[bar.O] = -4.16 (A28)

Then,

S/N = -10log(MSD) (A29)

Remarks: MSD is the mean-square deviation.

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (A30)

= 2.61 (A31)

MSD (QC=S) = 1/n [n.summation over (i=1)] [yi.sup.2] (A32)

2.61 = [y.sup.2] (A33)

y = [square root of 2.61] (A34)

y=1.62% is the predicted value of volumetric shrinkage for PP + 50 wt% wood.

Step 9: Analysis of variance (ANOVA)

i) Degrees of freedom (DOF)

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (A35)

Remarks: [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] is the total number of degrees of freedom, N is the number of experiments, [f.sub.x] is the number of degrees of freedom attributable to the considered factors, [k.sub.x] is the number of levels of each factor, and [f.sub.e] is the error on the degrees of freedom.

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (A36)

[f.sub.A]= [k.sub.A] - 1 = 4 - 1 = 3 (A37)

[f.sub.B] = [k.sub.B] - 1 = 4 - 1 = 3 (A38)

[f.sub.C] = [k.sub.C] - 1 = 4 - 1 = 3 (A39)

[f.sub.D] = [k.sub.D] - 1 = 4 - 1 = 3 (A40)

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (A41)

= 15 - 3 - 3 - 3 - 3 (A42)

= 3 (A43)

ii) Sum-of-squares computation

The sum of squares (SS) attributable to the overall experimental mean:

SS attributable to the mean = N x [bar.S]/N (A44)

= 16 x [(-7.73).sup.2] (A45)

= 956.93 (A46)

The total sum of squares:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (A47)

= 53.49 (A48)

Sum of squares for factor A:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (A49)

Remarks: [N.sub.Level xi] is the number of experiments at level i = 1, 2, 3, or 4.

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (A50)

This procedure is repeated for factors B, C, and D, and the sums of squares are as follows:

[SS.sub.B] =4 .54 d[B.sup.2] (A51)

[SS.sub.C] = 16.42 d[B.sup.2] (A52)

[SS.sub.D] = 5.11 d[B.sub.2] (A53)

Sum of squares for experimental error:

[SS.sub.e] = [SS.sub.T] - [SS.sub.A] - [SS.sub.B] - [SS.sub.C] - [SS.sub.D] (A54)

= 53.49 - 22.13 - 4.54 - 16.42 - 5.11 (A55)

= 5.29 [dB.sup.2] (A56)

iii) Mean-square variance for a control factor For factor A:

[MS.sub.A] = [SS.sub.A]/[f.sub.A] = 22.13/3 = 7.38 (A57)

For factor B:

[MS.sub.B] = [SS.sub.B]/[f.sub.B] = 4.54/3 = 1.51 (A58)

For factor C:

[MS.sub.C] = [SS.sub.C]/[f.sub.C] = 16.42/3 = 5.47 (A59)

For factor D:

[MS.sub.D] = [SS.sub.D]/[f.sub.D] = 5.11/3 = 1.70 (A60)

For mean square (error variance)

[MS.sub.Error] = [SS.sub.e]/[f.sub.e] = 5.29/3 = 1.76 (A61)

iv) The F-test

The F-ratio is given by the equation below. When F is much greater than 1, the effect of the control factor is large compared to the variance that is attributable to experimental error and interaction effects.

F = Mean square due to a control factor/Mean square due to experimental error = M[S.sub.X]/M[S.sub.Error] (A62)

These are some suggested generalised values for F-ratios:

* F<1: The experimental error outweighs the control effect; the control factor is insignificant and indistinguishable from the experimental error.

* F [approximately equal to] 2: The control factor has only a moderate effect compared to the experimental error.

* F>4: The control factor is strong compared to the experimental error and is clearly significant.

Therefore, for factor A:

[F.sub.A] = M[S.sub.A]/M[S.sub.e] = 7.38/1.76 = 4.18 (A63)

This procedure is repeated for factors B, C and D, and the F ratios are as follows:

[F.sub.B] = 0.86 (A64)

[F.sub.C] = 3.10 (A65)

[F.sub.D] = 0.97 (A66)

v) Calculate the percentage contribution (%P) to determine the most significant or least significant factors

% [P.sub.A] = S[S.sub.A]/S[S.sub.T] x 100% = 22.13/53.49 x 100% = 41.36% (A67)

This procedure is repeated for factors B, C, and D and the experimental error, and the percentage contributions are as follows:

% [P.sub.B] = 8.49% (A68)

% [P.sub.C] = 30.71% (A69)

% [P.sub.D] = 9.55% (A70)

% [P.sub.e] = 9.89% (A71)

vi) Analysis of variance (ANOVA) table for volumetric shrinkage of PP + 50 wt% wood

Factors   DOF(f)    SS      MS    F-Ratio    % P

4          3.00    22.13   7.38    4.18     41.36
B          3.00    4.54    1.51    0.86     8.49
C          3.00    16.42   5.47    3.10     30.71
D          3.00    5.11    1.70    0.97     9.55
Error      3.00    5.29    1.76             9.89


ACKNOWLEDGMENTS

The authors would like to thank Universiti Putra Malaysia for financial support through the Research University Grant Scheme (Project No: RUGS/05-02-12-1917RU).

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M.D. Azaman, (1,2) S.M. Sapuan, (1,3) S. Sulaiman, (1) E.S. Zainudin, (1,3,4) A. Khalina (5,6)

(1) Department of Mechanical and Manufacturing Engineering, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia

(2) School of Manufacturing Engineering, Universiti Malaysia Perlis, 02600 Arau, Perlis, Malaysia

(3) Laboratory of Biocomposite Technology, Institute of Tropical Forestry and Forest Products (INTROP), Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia

(4) School of Engineering and Design, Brunei University, Uxbridge Middlesex, UB8 3PH, UK

(5) Department of Biological and Agricultural Engineering, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia

(6) Aerospace Malaysia Innovation Centre, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia

Correspondence to: M.D. Azaman; e-mail: azaman@unimap.edu.my

DOI 10.1002/pen.23979

Published online in Wiley Online Library (wileyonlinelibrary.com).

TABLE 1. Specifications of injection-molding machine.

Parameters                            Units          Value

Maximum machine injection stroke      mm             176.8
Maximum machine injection rate        [cm.sup.3]/s   112.1
Machine screw diameter                mm              30
Maximum machine injection pressure    MPa             247
Maximum machine clamp force           MPa            79.80

TABLE 2. Material properties of lignocellulosic polymer composites.

                       PP + 40           PP + 50           PP + 60
                      wt% wood          wt% wood          wt% wood

Trade name            NCell 40         ISOFORM LIP        WPC-2-mv
                                         CPCW 50
Filler content           40                50                60
  (wt%)
Material           Semicrystalline   Semicrystalline   Semicrystalline
  structure
Melt flow rate            3                 5               3.81
  (g/10 min)
Melt temperature         190               185               190
  ([degrees]C)
Mold temperature         50                45                50
  ([degrees]C)
Aspect ratio              1                 1                 1
  (L/D) of
  Fillers

TABLE 3. Post-filling processing parameters.

Parameters                                   Values

Packing pressure           0-8[P.sub.inject]-0.95[P.sub.inject] MPa
Packing time                                 10-40 s
Mold surface temperature                 40-50[degrees]C
Cooling time                                 1040 s

TABLE 4. Process parameters and levels.

                                          PP + 40 wt% wood
                                               Levels

Factor   Description (unit)               1    2    3    4

A        Packing pressure (%)             80   85   90   95
B        Packing time (sec)               10   20   30   40
C        Mold temperature ([degrees]C)    40   45   50   55
D        Cooling time (sec)               10   20   30   40

         PP + 50 wt% wood    PP + 60 wt% wood
              Levels              Levels

Factor   1    2    3    4    1    2    3    4

A        80   85   90   95   80   85   90   95
B        10   20   30   40   10   20   30   40
C        40   45   50   55   40   45   50   55
D        10   20   30   40   10   20   30   40

TABLE 5. The combination of parameters in the orthogonal array
[L.sub.16][4.sup.4].

No. trial      Parameters

            A    B    C    D

1           80   10   40   10
2           80   20   45   20
3           80   30   50   30
4           80   40   55   40
5           85   10   45   30
6           85   20   40   40
7           85   30   55   10
8           85   40   50   20
9           90   10   50   40
10          90   20   55   30
11          90   30   40   20
12          90   40   45   10
13          95   10   55   20
14          95   20   50   10
15          95   30   45   40
16          95   40   40   30

TABLE 6. Results for residual stresses, volumetric shrinkage and
warpage and the corresponding S/N values for the investigated
materials.

                         PP + 40 wt% wood

         Residual
         stresses           Shrinkage           Warpage
           (MPa)               (%)               (mm)

Run   Average    S/N     Average    S/N     Average    S/N

1      46.39    -33.33   2.047     -6.22    0.0140    37.08
2      45.00    -33.06   1.968     -5.88    0.0742    22.59
3      52.42    -34.39   2.414     -7.65    0.0222    33.07
4      60.21    -35.59   2.991     -9.52    0.0235    32.58
5      47.64    -33.56   2.009     -6.06    0.0157    36.08
6      40.77    -32.21   1.667     -4.44    0.0539    25.37
7      41.02    -32.26   1.591     -4.03    0.0081    41.83
8      47.88    -33.60   1.967     -5.88    0.0081    41.83
9      31.44    -29.95   1.056     -0.47    0.0242    32.32
10     35.07    -30.90   1.317     -2.39    0.0352    29.07
11     37.91    -31.58   1.334     -2.50    0.0181    34.85
12     32.40    -30.21   1.042     -0.36    0.0142    36.95
13     28.76    -29.18   0.8243     1.68    0.0585    24.66
14     27.15    -28.68   0.8038     1.90    0.0545    25.27
15     20.49    -26.23   0.6299     4.01    0.0317    29.98
16     18.31    -25.25   0.5105     5.84    0.0276    31.18

                         PP + 50 wt% wood

         Residual
         stresses           Shrinkage           Warpage
           (MPa)               (%)               (mm)

Run   Average    S/N     Average    S/N     Average    S/N

1      29.12    -29.28    3.181    -10.05   0.0390    28.18
2      31.30    -29.91    3.560    -11.03   0.0335    29.50
3      24.48    -27.78    2.528    -8.06    0.0222    33.07
4      27.04    -28.64    2.934    -9.35    0.0177    35.04
5      23.01    -27.24    2.178    -6.76    0.0189    34.47
6      21.46    -26.63    2.021    -6.11    0.0179    34.94
7      29.64    -29.44    3.025    -9.61    0.0425    27.43
8      25.96    -28.29    2.576    -8.22    0.0442    27.09
9      26.17    -28.36    2.392    -7.58    0.0557    25.08
10     29.32    -29.34    2.884    -9.20    0.0381    28.38
11     21.02    -26.45    1.867    -5.42    0.0482    26.34
12     23.88    -27.56    2.137    -6.60    0.0577    24.78
13     31.75    -30.03    2.901    -9.25    0.0128    37.86
14     24.01    -27.61    2.009    -6.06    0.0266    31.50
15     23.31    -27.35    1.948    -5.79    0.0294    30.63
16     20.98    -26.44    1.708    -4.65    0.0445    27.03

                         PP + 60 wt% wood

         Residual
         stresses           Shrinkage           Warpage
           (MPa)               (%)               (mm)

Run   Average    S/N     Average    S/N     Average    S/N

1      57.26    -35.16    2.486    -7.91    0.0761    22.37
2      62.08    -35.86    2.747    -8.78    0.0685    23.29
3      65.86    -36.37    3.001    -9.55    0.0939    20.55
4      152.0    -43.64    9.933    -19.94   0.4415    7.10
5      59.77    -35.53    2.501    -7.96    0.0425    27.43
6      55.60    -34.90    2.723    -8.70    0.2188    13.20
7      65.93    -36.38    2.843    -9.08    0.0438    27.17
8      57.86    -35.25    2.582    -8.24    0.0633    23.97
9      57.86    -35.25    2.403    -7.62    0.2279    31.09
10     61.64    -35.80    2.516    -8.01    0.0351    29.09
11     138.5    -42.83    7.694    -17.72   0.2355    12.56
12     55.66    -34.91    2.351    -7.43    0.0887    21.04
13     49.23    -33.84    1.888    -5.52    0.0351    29.09
14     53.02    -34.49    2.041    -6.20    0.0441    27.11
15     41.19    -32.30    1.561    -3.87    0.0472    26.52
16     38.43    -31.69    1.424    -3.07    0.0749    22.51

TABLE 7. The responses of S-N ratios for in-cavity residual stress,
volumetric shrinkage and warpage.

                               Packing   Packing     Mold      Cooling
                               pressure   time    temperature   time

In-cavity   PP + 40  Level l   -34.09    -31.50     -30.59#    -31.12
residual      wt%    Level 2   -32.91    -31.21     -30.77     -31.85
stress        wood   Level 3   -30.66    -31.11#    -31.65     -31.03
                     Level 4   -27.33#   -31.17     -31.98     -31.00#
                     Variance    6.76      0.39       1.39       0.85

            PP + 50  Level 1   -28.9     -28.73     -27.2#     -28.47
              wt%    Level 2   -27.9     -28.37     -28.02     -28.67
              wood   Level 3   -27.93    -27.75     -28.01     -27.70
                     Level 4   -27.86#   -27.73#    -29.36     -27.74
                     Variance    1.04      1          2.16       0.97

            PP + 60  Level 1   -37.76    -34.94#    -36.15     -35.23
              wt%    Level 2   -35.52    -35.26     -34.65#    -36.95
              wood   Level 3   -37.2     -36.97     -35.34     -34.85#
                     Level 4   -33.08#   -36.37     -37.42     -36.52
                     Variance    4.68      2.03       2.77       0.38

                               Packing   Packing     Mold      Cooling
                               pressure   time    temperature   time

Warpage     PP + 40  Level 1    31.33     32.54      32.12      35.28#
              wt%    Level 2    36.28#    25.58      31.4       30.98
              wood   Level 3    33.30     34.93      33.12      32.35
                     Level 4    27.77     35.64#     32.03      30.06
                     Variance    8.51     10.06       1.72       5.22
            PP + 50  Level 1    31.45     31.40#     29.12      27.97
              wt%    Level 2    30.98     31.08      29.84      30.20
              wood   Level 3    26.14     29.37      29.19      30.74
                     Level 4    31.76#    28.49      32.18#     31.42#
                     Variance    5.62      2.91       3.06       3.45
            PP + 60  Level 1    18.33     27.50#     17.66      24.42
              wt%    Level 2    22.94     23.17      24.57      22.23
              wood   Level 3    23.45     21.7       25.68#     24.90#
                     Level 4    26.31#    18.66      23.11      19.48
                     Variance    7.98      8.84       8.02       5.42

                               Packing   Packing     Mold      Cooling
                               pressure   time    temperature   time

Volumetric  PP + 40  Level 1    -7.32     -2.77      -1.83#     -2.18#
shrinkage     wt%    Level 2    -5.1      -2.7       -2.07      -3.15
              wood   Level 3    -1.43     -2.54      -3.03      -2.57
                     Level 4     3.36#    -2.48#     -3.57      -2.6
                     Variance   10.68      0.29       1.74       0.97

            PP + 50  Level 1    -9.62     -8.41      -6.56#     -8.08
              wt%    Level 2    -7.68     -8.10      -7.54      -8.48
              wood   Level 3    -7.20     -7.22      -7.48      -7.17#
                     Level 4    -6.44#    -7.20#     -9.35      -7.21
                     Variance    3.18      1.21       2.79       1.31

            PP + 60  Level 1   -11.54     -7.25#     -9.35      -7.65
              wt%    Level 2    -8.49     -7.92      -7.01#    -10.06
              wood   Level 3   -10.19    -10.05      -7.9       -7.15#
                     Level 4    -4.66#    -9.67     -10.64     -10.03
                     Variance    6.88      2.8        3.63       2.91

The significance of bold indicate the highest S/N ratio. This is to
indicate the optimum combination of processing parameter conditions
for the minimisation of the three selected responses (residual
stress, shrinkage and warpages).

Note: The highest S/N ratio are indicated with #.

TABLE 8. The optimal combinations of parameters and the verification
of the simulation results.

                                     Factors

                                A    B    c    D    Predicted

Residual     PP + 40 wt% wood
  stresses   Values             95   30   40   40   20.63 MPa
             Levels             4    3    1    4
             PP + 50 wt% wood
             Values             95   40   40   30   20.06 MPa
             Levels             4    4    1    3
             PP + 60 wt% wood
             Values             95   10   45   30   31.12 MPa
             Levels             4    1    2    3
Shrinkage    PP + 40 wt% wood
             Values             95   40   40   10   0.58%
             Levels             4    4    1    1
             PP + 50 wt% wood
             Values             95   40   40   30   1.62%
             Levels             4    4    1    3
             PP + 60 wt% wood
             Values             95   10   45   30   0.99%
             Levels             4    1    2    3
Warpage      PP + 40 wt% wood
             Values             85   40   50   10   0.0064 mm
             Levels             2    4    3    1
             PP + 50 wt% wood
             Values             95   10   55   40   0.0150 mm
             Levels             4    1    4    4
             PP + 60 wt% wood
             Values             95   10   50   30   0.0156 mm
             Levels             4    1    3    3

                                Simulated   Deviation

Residual     PP + 40 wt% wood
  stresses   Values             19.33 MPa     6.30%
             Levels
             PP + 50 wt% wood
             Values             20.97 MPa     4.54%
             Levels
             PP + 60 wt% wood
             Values             46.51 MPa    49.45%
             Levels
Shrinkage    PP + 40 wt% wood
             Values             0.56%         3.45%
             Levels
             PP + 50 wt% wood
             Values             1.755%        8.33%
             Levels
             PP + 60 wt% wood
             Values             1.723%       74.04%
             Levels
Warpage      PP + 40 wt% wood
             Values             0.0059 mm     7.81%
             Levels
             PP + 50 wt% wood
             Values             0.0137 mm     8.67%
             Levels
             PP + 60 wt% wood
             Values             0.0233 mm    49.35%
             Levels

TABLE 9. ANOVA for in-cavity residual stresses.

Factors              Degrees of freedom, DOF   Sum of squares, SOS

PP + 40 wt% wood
  Packing pressure              3                    106.08
  Packing time                  3                      0.37
  Mold temperature              3                      5.47
  Cooling time                  3                      1.99
  Error                         3                     10.31
  Total                        15                    124.22
PP + 50 wt% wood
  Packing pressure              3                      3.06
  Packing time                  3                      2.87
  Mold temperature              3                      9.65
  Cooling time                  3                      2.97
  Error                         3                      3.14
  Total                        15                     21.69
PP + 60 wt% wood
  Packing pressure              3                     52.89
  Packing time                  3                     10.75
  Mold temperature              3                     16.94
  Cooling time                  3                     12.10
  Error                         3                     56.07
  Total                        15                    148.75

Factors              Mean squares, MS   F-ratio   % contribution

PP + 40 wt% wood
  Packing pressure        35.36          10.29        85.40
  Packing time             0.12           0.04         0.29
  Mold temperature         1.82           0.53         4.40
  Cooling time             0.66           0.19         1.60
  Error                    3.44                        8.30
  Total                                                100
PP + 50 wt% wood
  Packing pressure         1.02           0.97        14.10
  Packing time             0.96           0.91        13.22
  Mold temperature         3.22           3.07        44.48
  Cooling time             0.99           0.95        13.71
  Error                    1.05                       14.49
  Total                                                100
PP + 60 wt% wood
  Packing pressure        17.63           0.94        35.55
  Packing time             3.58           0.19         7.22
  Mold temperature         5.65           0.30        11.39
  Cooling time             4.03           0.22         8.14
  Error                   18.69                       37.70
  Total                                                100

TABLE 10. ANOVA for volumetric shrinkage.

Factors              Degrees of freedom, DOF   Sum of squares, SOS

PP + 40 wt% wood
  Packing pressure              3                    261.52
  Packing time                  3                      0.22
  Mold temperature              3                      7.94
  Cooling time                  3                      1.89
  Error                         3                     16.95
  Total                        15                    288.52
PP + 50 wt% wood
  Packing pressure              3                     22.13
  Packing time                  3                      4.54
  Mold temperature              3                     16.42
  Cooling time                  3                      5.11
  Error                         3                      5.29
  Total                        15                     53.49
PP + 60 wt% wood
  Packing pressure              3                    106.60
  Packing time                  3                     21.88
  Mold temperature              3                     30.72
  Cooling time                  3                     28.56
  Error                         3                     96.96
  Total                        15                    284.72

Factors              Mean squares, MS   F-ratio   % contribution

PP + 40 wt% wood
  Packing pressure        87.17          15.43        90.64
  Packing time             0.07           0.01         0.08
  Mold temperature         2.65           0.47         2.75
  Cooling time             0.63           0.11         0.66
  Error                    5.65                        5.87
  Total                                                100
PP + 50 wt% wood
  Packing pressure         7.38           4.18        41.36
  Packing time             1.51           0.86         8.49
  Mold temperature         5.47           3.10        30.71
  Cooling time             1.70           0.97         9.55
  Error                    1.76                        9.89
  Total                                                100
PP + 60 wt% wood
  Packing pressure        35.53           1.10        37.44
  Packing time             7.29           0.23         7.68
  Mold temperature        10.24           0.32        10.79
  Cooling time             9.52           0.29        10.03
  Error                   32.32                       34.05
  Total                                                100

TABLE 11. ANOVA for warpage.

Factors              Degrees of freedom, DOF   Sum of squares, SOS

PP + 40 wt% wood
  Packing pressure              3                    152.76
  Packing time                  3                    253.07
  Mold temperature              3                      6.09
  Cooling time                  3                     62.33
  Error                         3                     40.26
  Total                        15                    514.51
PP + 50 wt% wood
  Factors            Degrees of freedom, DOF   Sum of squares, SOS
  Packing pressure              3                     83.93
  Packing time                  3                     23.14
  Mold temperature              3                     24.67
  Cooling time                  3                     26.80
  Error                         3                     79.91
  Total                        15                    238.45
PP + 60 wt% wood
  Factors            Degrees of freedom, DOF   Sum of squares, SOS
  Packing pressure              3                    131.02
  Packing time                  3                    162.28
  Mold temperature              3                    151.73
  Cooling time                  3                     73.55
  Error                        15                    155.82
  Total                                               674.4

Factors              Mean squares, MS   F-ratio   % contribution

PP + 40 wt% wood
  Packing pressure        50.92          3.79         29.69
  Packing time            84.36          6.29         49.19
  Mold temperature         2.03          0.15          1.18
  Cooling time            20.78          1.55         12.11
  Error                   13.42                        7.83
  Total                                                100
PP + 50 wt% wood
  Factors            Mean squares, MS   F-ratio   % contribution
  Packing pressure        27.98          1.05         35.20
  Packing time             7.71          0.29          9.71
  Mold temperature         8.22          0.31         10.34
  Cooling time             8.93          0.34         11.24
  Error                   26.64                       33.51
  Total                                                100
PP + 60 wt% wood
  Factors            Mean squares, MS   F-ratio   % contribution
  Packing pressure        43.67          0.84         19.43
  Packing time            54.09          1.04         24.06
  Mold temperature        50.58          0.97         22.50
  Cooling time            24.52          0.47         10.91
  Error                   51.94                       23.11
  Total                                                100
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Author:Azaman, M.D.; Sapuan, S.M.; Sulaiman, S.; Zainudin, E.S.; Khalina, A.
Publication:Polymer Engineering and Science
Article Type:Report
Date:May 1, 2015
Words:9996
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