INVESTIGATION ON WARPAGE AND SINK MARK FOR INJECTION MOULDED PARTS USING TAGUCHI METHOD

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1 INVESTIGATION ON WARPAGE AND SINK MARK FOR INJECTION MOULDED PARTS USING TAGUCHI METHOD Omar Ahmed Mohamed, Swinburne University of Technology, Melbourne, Australia Syed Hasan Masood, Swinburne University of Technology, Melbourne, Australia Abul Saifullah, Swinburne University of Technology, Melbourne, Australia Jahar Lal Bhowmik, Swinburne University of Technology, Melbourne, Australia Abstract Injection molding is a complex process for many production engineers as it involves selection of many process parameters to produce quality products to meet customer requirements. Determination of the optimal process parameters in injection moulding is an important design task as it influences part quality, production rate, and production cost and energy consumption. The purpose of this paper is to investigate the effect of selected process parameters in injection moulding on part quality. The paper applies Taguchi s parametric design and analysis of variance (ANOVA) technique to study the effect of process settings of plastic injection molding on part quality. Experimental data are used to identify the relationship between the injection molding process parameters and product quality. Mold surface temperature, melt temperature, mold open time and ejection temperature are selected as the process control parameters. and sink mark depth are selected as the multi-product quality characteristics. Introduction Plastic injection molding is the most common process for the economic mass production of plastic parts of a variety of geometries and shapes [1]. Production of items by this process involves plastic pellets to be heated inside a barrel, melted, and then injected into a mold cavity where it cools and eventually hardens into a shape similar to that of the cavity. [2]. Plastic Injection molding process has been a challenging process for many production engineers to produce quality products to meet customer requirements. It is a complex production process due to many parameters that must be considered before manufacturing a part [3]. The determination of optimal molding process parameters is an important task for production engineers [4]. One of the major problems facing injection molders today is how to determine the final optimal injection molding process parameters. Incorrect or conflicting input process parameters will cause problems such as poor quality of the product, warpage, shrinkage, decreased dimensional precision, unacceptable wastes, large amounts of scrap, low productivity; increased cycle time and high cost [5]. Injection molding process parameters such as packing pressure, packing time, cooling time, mold temperature and melt temperature are some of the significant parameters in injection molding process design [6]. Decreasing packing pressure always leads to short filling causing weld lines, meld lines and air traps. Besides, increasing packing pressure can decrease the shrinkage and increase the product density. But if the packing pressure is too high, the product will warp and cannot drop down easily [7]. Increasing packing time, however, can increase viscosity and decrease melt temperature causing shear stress [8]. On the other hand, reducing packing time contributes to shorter cycle time that increases productivity rates. Thus, determining the appropriate packing pressure and packing time can provide enough melt volume improving the product quality by reducing the shrinkage and warpage. Furthermore, high mold temperature leads to reduce crack in the product. Increasing melt temperature leads to low viscosity, thus lower shear stress and cavity pressure. But it leads to long cycle time, which results in lower productivity rates [9, 10]. Moreover, high melt temperature leads to unbalanced shrinkage. Hence, warpage caused by unbalanced shrinkage may also be amplified. Therefore, finding the optimized injection molding process parameters is highly desirable. The objective of this paper is to apply Taguchi method and analysis of variance (ANOVA) technique to study the effect of four process parameters of injection molding machine on product quality and performance, and subsequently, reduce the production process costs and defects. The four process parameters considered in this study are the mold temperature, melt temperature, mold opening time and injection temperature. Problem Formulation Most product defects occur due to parameter settings which are not proper in plastic injection moulding machine. Figure 1 shows a cause and effect diagram of various process parameters in the product. Changing every parameter always produces a different quality of product in terms of properties. In previous years, trial and error methods were used to determine proper process parameters in plastic injection molding [11]. However, these methods are for a limited number of input and output parameters and it is not suitable for complex parts such as plastic part in our case study, where the most SPE ANTEC Indianapolis 2016 / 1675

2 important aspect is to minimize the production cost whilst keeping the quality at the highest level in order to manufacture parts suitable for assembly [12]. This can be achieved using design of experiment and response surface methodology to look at non-linear interactions between input variables and a set of criteria that can define part quality. Figure 1. Cause and effect diagram Design of Plastic Part In this study, a plastic part has been designed using Pro/Engineer Wildfire 5 as shown in Figure 2 where the part is of 2 mm in thickness, 195 mm in height and 119 mm in width. The IGES (Initial Graphics Exchange Specification) Computer Aided Design (CAD) model of the plastic part has been imported to Autodesk Moldflow Insight (AMI) to perform analysis and design cooling channel. be considered as the main problem during manufacturing process of thin plastic parts. Plastic parts that are distorted can also result in poor surface finish, inaccuracies dimensional inaccuracies and poor assembly [12]. is caused by the differential shrinkage of the molded plastic part since plastic polymer will shrink more at higher temperatures than at lower temperatures. In addition, sink mark or void occurs as a local surface depression or a vacuum bubble in the molding interior due to material shrinkage without enough compensation. Sink Marks and voids occur at regions with high local shrinkage [12]. Sink marks typically occur in moldings with thicker areas. Also, it happens because there may be possibility of unbalanced heat removal in cooling. The presence and location of sink mark or void depends on appearance of the opposite surface. The experimental design proposed by Taguchi based on orthogonal arrays can be used to reduce time, cost and the number of experiments under permissive reliability. Usually, orthogonal arrays are selected based on the number of parameters and the number of levels. In this study, the design of experiment via Taguchi method is selected to determine the best set of parameters that have the greatest influence on the part quality and to rank each influence on part quality. In injection molding process there are a number of factors that have significant effects on part quality. In this experiment, the factors to be studied are only mold surface temperature, melt temperature, mold open time and ejection temperature. Table 1 shows the values and levels of mold surface temperature, melt temperature, mold open time and ejection temperature for two thermoplastics: ABS and polypropylene (PP). All levels have been selected based on literature review and Moldflow material library. Table 1. Process parameters and their levels Figure 2. CAD Model of the plastic part with cooling channels Design of Experiment and sink mark are important quality characteristics in injection molded product. can Code Factors Material Levels A B C D Mold surface temperature ( o C) Melt temperature ( o C) Mold open time (s) Ejection temperature ( o C) ABS PP ABS PP ABS PP ABS PP In this experiment, according to the four experimental factors (A, B, C, D) and their levels, this research applies SPE ANTEC Indianapolis 2016 / 1676

3 an L9 (34) orthogonal array to assign four factors to the columns to achieve a complete design in which (A), Mold Surface Temperature, (B), Melt Temperature, (C), Mold Open Time and (D), Ejection Temperature are located in columns 1 to 4, respectively. This selection of the orthogonal array (L9) is due to its suitability for three levels with four factors. The design of L9 (34) orthogonal array is shown in Table 2. Table 2. L9 (3 4 ) Orthogonal Array Variance Trial No Control Factor A B C D Results and Discussion Taguchi method recommends the use of the signal-tonoise (S/N) ratio to determine the best setting of the process parameters arrangement. In order to select the appropriate formulations of quality characteristics, Taguchi has utilized the Signal-to-Noise ratio. The signalto-noise (S/N) ratio is one of the measurement equation for quality characteristics to determine the optimal process parameters. The warpage and sink mark values for PP and ABS parts exploited from Moldflow software are listed in Table 3. The signal-to-noise ratio can be classified into three categories: the nominal the best, the smaller the better, and the larger the better. Since the aim of this research is to reduce warpage and sink mark depth within the optimal process conditions, the smaller the better quality characteristic has been chosen and it is defined as follows: The S/N ratio obtained for warpage and sink mark depth for ABS and PP are also shown in Table 3. Table 4 shows the values of warpage and sink mark response estimated for ABS and PP parts. Figure 3 show the graphical display of main effect of parameters on warpage and sink mark for ABS material, and Figure 4 show similar displays for PP material. From Table 4, the highest level value indicates the best level setting for each factor. The S/N response results in terms of warpage using ABS shows that the most significant factors that have an effect on warpage are mold open time followed by melt temperature, mold surface temperature and ejection temperature accordingly as shown in Figure 3a. On the other hand, the most significant factor that has an effect on sink mark estimate is melt temperature. Other factor such as mold surface temperature, mold open time and ejection temperature have slight effect on sink mark estimate as can be seen in Figure 3b. The S/N response results in terms of warpage with the use of polypropylene material indicates that the most significant factors that have an effect on warpage are mold surface temperature followed by melt temperature, mold open time and ejection temperature accordingly as shown in Figure 4a. The most important factors that have an effect on sink mark estimate in the polypropylene material are mold surface temperature followed by melt temperature, mold open time but ejection temperature has a slight effect on sink mark estimate as shown in Figure 4b. S/N = 10LOG (MSD), (1) MSD = 1 n 4 3 Y 2 256, (2) where, MSD: is the mean square deviation. Y: Represents the observation and Yi is the value of warpage or sink mark depth for the i test n: is the number of tests N: Is the number of tests in one trial. Figure 3. The main effect of parameters on (a) warpage, and (b) sink mark for ABS material. SPE ANTEC Indianapolis 2016 / 1677

4 Table.4. Response table of S/N ratio ABS Level Sink Mark Estimate A B C D A B C D Polypropylene Sink Mark Estimate Level A B C D A B C D Table 5. ANOVA results for ABS material Factor Coefficient Standard error T- statistic P- value Const A B C D Sink mark Const A B C D Figure 4. The main effect of parameters on (a) warpage, and (b) sink mark for polypropylene material. Analysis of Variance (ANOVA) The Analysis of variance (ANOVA) technique was applied to observe the effect of each parameter affecting the quality characteristic. The statistical significance was checked by F-test and the analysis of variance (ANOVA) is shown in Table 5 and Table 6. Larger values T-statistic for the term indicate significance. P-value (probability value) can be used to test the significance of each of the regression coefficient. If the P-value is smaller than 0.05, then the factor has a significant influence on the warpage and sink mark. In this case, melt temperature (B) and mold open time (C) are significant factors for warpage using ABS material. While, mold temperature (A), melt temperature (B), and ejection temperature (D) have significant effect on sink mark for ABS material as shown in Table 5. It can be seen from Table 6 that mold temperature (A) has a significant effect on the warpage and sink mark for Polypropylene material. Other factors have little influence on these properties. Table 5. ANOVA results for ABS material Factor Coefficient Standard error T- statistic P- value Const A B C D Sink mark Const A B C D Optimum Process Settings From the S/N ratio response as shown in Table 4, the best combination of levels for all factors for both materials can be determined by selecting the highest value of response from each factor. Table 7 and Table 8 show SPE ANTEC Indianapolis 2016 / 1678

5 the best combination of parameters for ABS and PP respectively. Table 7. Optimum combination of parameters for ABS material Factor Sink Mark depth Mold Temperature 40 o C 80 o C Melt Temperature 240 o C 240 o C Mold Open Time 6 s 6 s Ejection Temperature 90 o C 136 o C Table 8. Optimum combination of parameters for PP material Factor Sink Mark depth Mold Temperature 30 o C 50 o C Melt Temperature 235 o C 195 o C Mold Open Time 6 s 6 s Ejection Temperature 127 o C 152 o C Confirmation Experiments The optimized parameters can lead to minimum warpage and minimum sink mark depth in ABS and PP parts. To verify that, the optimized process parameters were used in the simulation under Moldflow simulation environment. From Figure 5 and Figure 6, the warpage and sink mark depth analyses results indicate that the values of warpage and sink mark depth after using optimized parameters were mm and mm respectively, which means that the value of warpage was improved by 4.51% and the value of sink mark depth was improved by % in comparison to the initial warpage and sink mark results mm and mm as shown in Figure 5(a) and Figure 6(a) respectively. Similar simulation was carried out for polypropylene parts, and the results for warpage and sink mark depth analyses indicate that they were improved to mm and mm respectively. Thus they are shortened by 1.56 % in terms of warpage and by 3.16 % in terms of sink mark depth as compared to the initial warpage and sink mark. Table 9 shows a summary of all simulation results for both materials. The values of warpage and sink mark depth after the experiments have improved because some of the process parameters before the optimization are set out with the recommended values of the machine manual. Conclusions Taguchi method and ANOVA technique were used to determine the significant factors that contribute to warpage and sink mark estimate for injection-molded part. From the results, it can be concluded that 40oC mold surface temperature, 240oC of melt temperature, 6 seconds of mold open time and 90oC of ejection temperature were the best combination in terms of warpage and 80 oc of mold surface temperature, 240 oc of melt temperature, 6 seconds of mold open time and 136 oc of ejection temperature were the best combination of parameters in terms of sink mark estimate. From ANOVA results, it can be concluded that: 1. In ABS, melt temperature has the highest percentage affecting on the warpage of the part followed by mold open time and mold surface temperature. But ejection temperature has the lowest effect on warpage. 2. In ABS, melt temperature contributes the highest percentage affecting on sink mark estimate. But other factors have very slight contributions in terms of sink mark estimate. 3. In polypropylene, mold surface temperature plays the most significant factor affecting on the warpage of the product followed by melt temperature and mold open time. But ejection temperature has the lowest effect on warpage. 4. In polypropylene, mold surface temperature has the highest percentage affecting the sink mark depth followed by melt temperature and mold open time. The ejection temperature contributes slightly in term of sink mark depth. This study has shown that Taguchi method is an effective tool to solve the quality problems in plastic injection molding leading to minimum warpage and sink mark. References [1] Viana J. C; Ribeiro C.; Optimization of injection moulded polymer automotive components; New Trends and Developments in Automotive System Engineering, InTech, Rijeka (2011). [2] Szeteiová, Katarína: Automotive materials plastics in automotive markets today. In: Materials Science and Technology [online], Vol. 10, No. 3, pp (2010). [3] Shingo, S. A revolution in manufacturing: The SMED system. Portland, Productivity Inc. (1985) [4] Pötsch, G. & Michaeli, W. Injection molding: An introduction. (2nd ed.), Munich: Hanser Verlag. (2008). [5] Beanmont, J., Nagel, R & Sherman, R. Successful Injection Molding: Process Design and Simulation. Distributed in the USA and in Canada by Hanser Gardner Publications, (2002). [6] Mok, S. L. and Kwong, C. K., "Review of Research in the Determination of Process Parameters for Injection Molding, Journal of Advances of Polymer Technology, 18(3), (1999). [7] P.K. Bharti, Recent Methods for Optimization of Plastic Injection Moulding Process A Retrospective SPE ANTEC Indianapolis 2016 / 1679

6 and Literature Review, International Journal of Engineering Science and Technology Vol. 2(9), (2010). [8] Pandelidis I, Zou Q. Optimization of injection molding design (Part II): Molding conditions optimization [J]. Polym Eng Sci, 30(5): (1990). [9] Wen-Chin Chen, Gong-Loung Fu, Pei-Hao Tai, Wei- Jaw Deng, Process parameter optimization for MIMO plastic injection molding via soft computing. Expert Systems with Applications, (SCI, IF=2.596), 36(2), Part 1, (2009). [10] Plastic Mold Solution& Troubleshooting. Plastic Injection Pressure- Injection Molding Condition, March. 5 (2011). [11] Castro, C.E., Cabrera-Rios M, Castro JM, Lilly B Multiple Criteria Optimization With Variability Considerations in Injection Molding, Polym Eng. Sci. (2007). [12] Kazmer, D.O., Injection Mold Design Engineering, Hanser Gardner Publications, pp (2007). [13] Ozcelik, B. & Erzurumlu, T. Comparison of the warpage optimization in the plastic injection molding using ANOVA, neural network model and genetic algorithm. Journal of Materials Processing Technology, 171(3), (2006). Table 9. Summary of results for selected materials Material Unimproved warpage value (mm) Improved warpage value (mm) improved rate (%) Unimproved sink mark depth value (mm) Improved sink mark depth value (mm) Sink mark depth improved rate (%) ABS PP Figure 5. result on ABS with, (a) before and (b) after, optimizing process parameters Figure 6. Sink mark depth results on polypropylene with, (a) before and (b) after, optimizing process parameters SPE ANTEC Indianapolis 2016 / 1680

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