Optimization of Warpage Defect in Injection Moulding Process using ABS Material

A. H. Ahmad1* Z. Leman2, M. A. Azmir1, K. F. Muhamad1, W.S.W. Harun1, A. Juliawati1, A.B.S.

Alias1

1Faculty of Mechanical Engineering, Universiti Malaysia Pahang, Lebuhraya Tun Razak, 26300 Kuantan, Pahang, Malaysia.

2Department of Mechanical and Manufacturing Engineering, Universiti Putra Malaysia, 43400 UPM, Serdang Malaysia.

Email: asnul@ump.edu.my1, zleman@eng.upm.edu.my2

Abstract

Plastic injection moulding process produces various defects such as warpage, sink marks, weld lines and shrinkage. The purpose of present paper is to analyze the warpage defect on Acrylonitrile Butadiene Styrene (ABS) for selected part using FEA simulation. The approach was based on Taguchi’s Method and Analysis of Variance (ANOVA) to optimize the processing parameters namely packing pressure, mould temperature, melt temperature and packing time for effective process. It was found that the optimum parameters for ABS material are packing pressure at 375 MPa, mould temperature at 40 0C, melt temperature at 200 0C and packing time at 1 s. Melt temperature was found to be the most significant factor followed by packing time and mould temperature. Meanwhile, packing pressure was insignificant factor contributing to the warpage in present study.

1. Introduction

One of the plastic processing techniques is using injection moulding machine. The injection moulding process actually is the most practical and cost effective to produce plastic products [1]. Plastic injection moulding defects such as warpage, sink marks, weld lines and shrinkage are the common defects occur in the plastics injected parts.

The main cause of warpage is commonly known as the variation in shrinkage towards injection process of thin-shell plastic parts [2]. Thick sections cool slower than thin sections. The thin section first solidifies, and the thick section is still not fully solidified. As the thick section cools, it shrinks and the material for the shrinkage comes only from the unsolidified areas, which are connected, to the already solidified thin section. In practice, the dimensions, potential for warpage and internal stress level for a plastics part will influenced by a variety of material,

part geometry, tooling and processing related factors [3].

Parts with thick wall are most difficult to cool and pack. Thicker sections take longer to cool and required additional packing [1] .When parts have both thick and thin sections, gating into the thick section is preferred because it enables packing of the thick section, even if the thinner sections lead to shrinkage related internal stresses in the wall thickness regions. The shrinkage part easily transform into warpage if the parameter settings are not well control [4].

The warpage and shrinkage is understood as the process of non-uniform (heterogeneous) change of the geometrical dimensions of products in time resulting in a change (distortion) of their original form [5]. However, the researchers are using different parameter settings according to the part and raw material available thus it will contribute to the different results compare to each other.

The scope of this study is focusing on the simulation of warpage defect on the raw materials including determination of the effective parameters that contribute to the defect, the selection of the orthogonal arrays (OAs) and determination of the optimum parameter. The selection of the orthogonal arrays (OAs) depends on the level and parameter involved thus the 3 levels and 4 parameters were chosen.

The chosen parameters and level influenced the type of orthogonal arrays and the Taguchi L18 orthogonal arrays were used. Finally, the optimum parameters were determined by exploiting S/N ratio and ANOVA.

2. Experiment Set Up 2.1 Tools and Material

A series of simulations were conducted using Moldflow 2007 software to observe the warpage defect on the part. The simulation processes start with the development of the Computer Aided Design

2009 Third Asia International Conference on Modelling & Simulation

978-0-7695-3648-4/09 $25.00 © 2009 IEEE

DOI 10.1109/AMS.2009.120

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(CAD) data using Solidworks software. The selected part for this study is identification card holder where it was chosen because it could easily deform the warpage effect. The Acrylonitrile Butadiene Styrene (ABS) material was used for the experiment. The material properties for ABS are shown in Table 1 [6]. Table 1 Material Properties for ABS Material Structure Amorphous Melt Temperature (0C) 200~240 Mould Temperature(0C) 40~80 Max Shear Stress (MPa) 0.3 Max Shear Rate (1/s) 50000 Melt Density (g/cm3) 0.94752 Solid Density (g/cm3) 1.0432 Elastic Modulus (MPa) 2240 2.2 Selection of the Parameters

The selection of the parameters involved in this experiment based on the literature studies that have been made before. Based on the literature, a lot of parameters influence the warpage defect such as filling speed, melt temperature, packing time, cooling time, injection time, injection pressure, packing pressure and mould temperature. Huang et al., [7] claims that, the most effective factors contributes warpage are ranks as follows, packing pressure, mould temperature, melt temperature and packing time. The parameters and their levels are shown in Table 2. To determine the best set of parameter among the effective by reducing the number of experiments, the Taguchi method has been chosen [8]. Hence, the selection of the factors that will affect warpage, selection of the factor levels and selection of orthogonal array (OA) based on Taguchi method needed. From the number of factors and levels that have been selected, the L18 was chosen as an OA because it suitable for 4 factors with 3 levels. The L18 OA has 18 numbers of trial runs. The L18 OA are presented in Table 3. Table 2 The parameter for 3 levels of selected factors Factors Level 1 Level 2 Level 3 Packing Pressure, A (MPA) 300 375 450 Mould Temp., B (0C) 40 60 80 Melt Temp., C (0C) 200 220 240 Packing Time, D (s) 0.6 0.8 1.0

2.3 Simulation Approach Finite Element (FE) analysis of the part is performed using the commercial MoldFlow plastic insight software (MPI) to carry out all the injection moulding experiment. This software uses the finite elements and finite difference methods to calculate a series of mathematical functions representing the moulding process. This simulation provide information such as distribution and variation of temperature, pressure, flow rate, skin property, molecule orientation, shear stress and shear rate of the material in filling, packing and cooling stages. The results together with the moulding condition can be useful in product and mould design, where it may include the optimal gate location and runner size, prediction of weld line location, shrinkage and warpage. In addition the moulding conditions of the injection process can be optimized. The simulation started with the conversion process from CAD data into CAE data. The development of the CAD data before that used Solidworks software was transferred into the Moldflow plastics insight software. Moldlfow plastics insight software has the capability to convert directly the CAD data from Solidworks software. 3. Results and Discussions 3.1 S/N ratio Approach

The S/N ratio approach measure the quality

characteristic deviating from the desired values. The S/N ratio approach using the average values to convert the experimental results into the value for the evaluation characteristic in the optimum parameter analysis. The S/N ratios can be defined as [8]:

η = -10 log (M.S.D.) (1)

where M.S.D. is the mean-square deviation for the output characteristics.

The S/N ratio characteristic can be divided into three stages: the nominal-the better, the smaller the better and the higher the better when the quality characteristics are continuous for engineering analysis [1]. Since the objective of this study is to reduce the shrinkage problem through optimum parameters in injection moulding process, the smaller-the better quality characteristic was employed in this study. The M.S.D. for the smaller the better quality characteristic can be expressed by [8]:

M.S.D. = 1/N (Yi2) (2)

where Yi is the value of the warpage for the ith test. N is the total number of data points. Meanwhile to

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Table 3: The shrinkage and S/N ratio values _____________________________________________________________________________________________ Trial no. Packing Mould Melt Packing Warpage(mm) S/N (dBi) Pressure, Temp., Temp., Time A (MPa) B(0C) C(0C) D(s) 1 300 40 200 0.6 0.4679 6.597 2 300 60 220 0.8 0.4036 7.881 3 300 80 240 1.0 0.4469 6.996 4 375 40 200 0.8 0.3136 10.072 5 375 60 220 1.0 0.3755 8.508 6 375 80 240 0.6 0.4908 6.182 7 450 40 220 0.6 0.4046 7.859 8 450 60 240 0.8 0.4382 7.167 9 450 80 200 1.0 0.3608 8.855 10 300 40 240 1.0 0.3969 8.026 11 300 60 200 0.6 0.3823 8.352 12 300 80 220 0.8 0.4328 7.274 13 375 40 220 1.0 0.3455 9.231 14 375 60 240 0.6 0.4646 6.658 15 375 80 200 0.8 0.3862 8.264 16 450 40 240 0.8 0.4211 7.512 17 450 60 200 1.0 0.3205 9.883 18 450 80 220 0.6 0.4521 6.895

calculate minimum warpage defect, it can be expressed by [1]: W1op1+W2op2+W3op3+W4op4 – 3 x (Y) (3) where Wop is the optimum warpage value for its level. Y is the total defect for warpage in the cycle.

The warpage response diagram for each parameter at level 1(minimum), level 2(medium) and level 3(maximum) were created by utilizing the S/N ratio values. The values obtained by this process are recorded in form of the graph in Figure 1:

Figure 1: S/N response diagram

The best set of combination parameters can be determined by selecting the level with the highest

value of each factor. Thus, the result obtained are Packing Pressure A, level 2(A2), Mould Temperature B, level 1(B1), Melt Temperature C, level 1(C1) and Packing Time D, level 3(D3). 3.2 Analysis of Variance (ANOVA)

In order to determine the significant factors that contribute to the warpage, the result have been analyzed using Analysis of Variance (ANOVA). The ANOVA concept involving the relative percentage contribution among the factor is determined by comparing their relative variance. The ANOVA will compute the quantities such as degree of freedom (f), sum of squares (S), variance (V), F-ratio (F), and percentage contribution (P). The results can be summarized in Table 4. Table 4 ANOVA table Source f S(10-3) V(10-3) F P(%) P.Pressure,A 2 2.30 1.15 2.02 5.38 Md.Temp,B 2 4.70 2.35 4.12 10.98 Mt. Temp,C 2 15.30 7.65 13.42 35.75 P.Time,D 2 14.80 7.40 12.98 35.58 Error 10 5.70 0.57 12.31 Total 18 42.80 100

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Because on the F.10(2,10)=2.9245 [8], it is obvious to conclude that mould temperature(B), melt temperature(C) and packing time(D) significantly affect the warpage defect with 90% confident intervals. The percentage weight for each factor shows the influence factor that contributes to the warpage defect. The melt temperature contributes the most which is 35.75% meanwhile for the packing time, it contribute 35.58% which is the second factor that ranks as the influence factor for warpage defect. The mould temperature contributed 10.98% and lastly packing pressure just contributed 5.38% and it can be said that the packing pressure is not the significant factor for the warpage defect in this study. The error values are quite high which is contributed 12.31% where definitely influenced the warpage values in this study. 3.3 Effect of Processing Factors The effects of processing factors for all parameters are shown in Figure 2. The trend of the warpage defect for each parameter is similar to each other except packing pressure and packing time. When the melt temperature and mould temperature parameter settings increased, the warpage defect will significantly increase. Meanwhile for the packing pressure and packing time, the warpage decreased when the parameter settings increased.

(a) (b)

(c) (d) Figure 2: Processing factors for (a) packing pressure, (b) mould temperature, (c) melt temperature and (d)

packing time

Based on Huang [2], the warpage defect will increase significantly when the packing pressure was set more than 85% of the injection pressure value, and it refers to 425MPa for packing pressure value in this study. It shows that if the packing pressure value is set beyond 425MPa the warpage value will increase and to get smaller warpage value the packing pressure need to be set not exceed than 425MPa. Meanwhile, the warpage values will increase significantly when the packing time was set on the range 1-2 seconds [2]. The maximum packing time for this study was set 1 second and the simulation not exceed beyond that limit. The small warpage values occurred when the packing time increased not exceed more than 1 second thus it give the bright understanding that to get smaller warpage defect, the packing time setting need to be set at 1 second. The other parameters involved, mould temperature and melt temperature; the warpage defect will increase constantly if the parameter settings for these two parameters increase [2]. The conclusion for these two parameters is they needs to be set at the lowest level value to get the smaller warpage defect.

3.4 Verification Test By using the optimum parameters, the minimum warpage was estimated based on the equation 3. The calculations for minimum warpage are shown as follows: Minimum warpage =A2 + B1 + C1 + D3 – 3 x (Y) =0.3910+0.3816+0.3709+0.3704 – 3(0.4058) =1.5139 – 1.2174 =0.2965mm Meanwhile for the minimum warpage from simulation is 0.2780mm. Thus the error between simulation and calculated value is 6.65%. 4. Conclusion In this study, the warpage for ABS material show the different values when the parameter settings were changed. The used of Taguchi’s OAs simplified the experiment runs and ANOVA shows the influenced factor that contributed to the warpage defect. The optimum parameters for the ABS material are packing pressure at level 2(375MPa), mould temperature at level 1(400C), melt temperature at level 1(2000C) and packing time at level 3(1s). However among these factors, packing pressure is not a significant factor. Meanwhile for the processing factors, it shows the warpage value for mould temperature and melt temperature was increased if the parameter settings for

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its factor increased but in versus warpage values for packing time and packing pressure decrease if the parameter settings increased. 5. References [1]Hasan Oktem, Tuncay Erzurumlu, and Ibrahim Uzman, 2007. Application of Taguchi optimization technique in determining plastic injection molding process parameters for a thin-shell part. Materials and Design 28, 2007 1271–1278. [2]Ming-Chih Huang, Ching-Chih Tai, 2001. The effective factors in the warpage problem of an injection-molded part with a thin shell feature. Materials Processing Technology 110(2001) 1-9. [3]Robert A. Malloy, 1995, Injection Moulding Processes 3rd Edition, Canada, Prentice Hall. [4]S.H. Tang , Y.J. Tan, S.M. Sapuan, S. Sulaiman, N. Ismail, and R. Samin, 2007. The use of Taguchi method in the design of plastic injection mould for reducing warpage. Journal of Materials Processing Technology 182, 2007 418–426. [5]Hossein Hosseini, Boris Vasilich Berdyshev, Arjom and Mehrabani-Zeinabad, 2006, A solution for warpage in polymeric products by plug-assisted thermoforming. European Polymer Journal 42, 2006 1836-1843. [6]Moldflow2007.User manual Accessed on 13 February 2008. [7]Chiaming Yen, J.C. Lin, Wujeng Li, and M.F. Huang, 2006. An adductive neural network approach to the design of runner dimensions for the minimization of warpage in injection mouldings. Journal of Materials Processing Technology 174 , 2006 22–28. [8]Ranjit K. Roy, 1990. A primer on the taguchi method.Van Nostrand Reinhold, New York, USA.

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