 Advances in Technology Innovation , vol. 1, no. 2, 2016, pp. 46 - 49 46 Copyright © TAETI Study of Injection Molding Warpage Using Analytic Hierarchy Process and Taguchi Method Dyi-Cheng Chen * , Chen-Kun Huang Department of Industrial Education and Technology , National Changhua University of Education, Changhua, Taiwan. Received 22 February 2016; received in revised form 09 April 2016; accept ed 12 April 2016 Abstract This study integrated Analytic Hierarchy Process and Taguchi method to investigate into injection mold ing warpage. The warpage important factor will be elected by Analytic Hie rarchy Process (AHP), the AHP h ierarchy analysis factor fro m docu ments collected and aggregate out data, then through the expert questionnaire delete lo w weight factor. Finally, we used Taguchi quality engineering method to decide injection molding optimized co mbination factors. Furthermo re, the paper used injection pressure, holding pressure, holding time, mo ld temperature to analyze four factors, three levels Taguchi design data. Moreov er, the paper discussed the reaction of each factor on the S / N ratio and analysis of variance to obtain the best combination of minimal warpage. Keywor ds : Injection molding, Analytic Hierarchy Process (AHP), taguchi method 1. Introduction Plastic molding methods are injection molding, extrusion molding, blow molding, co -injection molding method, gas -assisted molding method, of which the injection molding method is the most widely used plastic molding technology . Kamaruddin [1] used Taguchi to improve mixed plastic products . The analysis of the results shows that the optimal combination for low shrinkage are low melting temperature, high injection pressure, low holding pressure, long holding time and long cooling time. Shuaib [2] performed to determine the factors that contribute to warpage for a thin shallow injection -molded part. The process used Taguchi and ANOVA technique. The result shows that by S/N response and percentage contribution in ANOVA, packing time has been identified to be the most significan t factors on affecting the warpage on thin shallow part. Radhwan et al. [3] applied Taguchi method for the optimization of selected process parameters such as the mold te mperature, me lt te mperature, packing pressure, packing time, and cooling time. The S/N ratio and analysis of variance were utilized to see the most significant factors contributing to shrinkage. Nasir et al. [4] designed mold in single and dual type of gate in order to investigate the deflection of warpage for thick component in injection molding process. Opasanon and Lertsanti [5] imple mented the analytic hierarchy process (AHP) to evaluate and rank the importance of the logistics issues according to the needs and requirements of the company’s policy makers. Four criteria considered in the AHP include cost, responsiveness, reliability, and utilization. Kil et al. [6] study to identify the ma jor variables identified as important for considering the stabilization of slope revegetation based on hydro seeding applications and evaluate weights of each variable using the analytic hie rarchy process (AHP). This study integrated Analytic Hierarchy Process and Taguchi method to investigate into injection molding warpage. 2. Results and Discussion of AHP and Taguchi Method 2.1. Analytic Hierarchy Process (AHP) In this study, injection mold ing gather relevant informat ion, collate and analyze the relevant factors. As shown in the present study hierarchica l structure shown in Fig. 1. In this study, interviews the way interviews professors fro m this and related industry contains several interviews with scholars States to carry out * Corresponding aut hor, Email: dcchen@cc.ncue.edu.tw http://xueshu.baidu.com/s?wd=author%3A%28Nasir%2C%20S.M.%29%20&tn=SE_baiduxueshu_c1gjeupa&ie=utf-8&sc_f_para=sc_hilight%3Dperson Advances in Technology Innovation , vol. 1, no. 2, 2016, pp. 46 - 49 47 Copyright © TAETI private visits to the volume. Fig. 1 Hierarchy architecture diagram Using Microsoft Excel software to analyse all questionnaires, one of the factors to estimate the impact of the overall configuration of the inner surface of the weight value, you can understand the factors within a ll facet degree of importance the key factors for overall warpage of inject ion mo lding, as a result as shown in table 1. Table 1 The overall weight table Main weight Secondary eight Overall weight A. pressure A1 0.52 0.25 (1) 0.48 A2 0.34 0.16 (2) A3 0.12 0.06 (8) B. time B1 0.44 0.13 (3) 0.29 B2 0.19 0.05 (9) B3 0.22 0.06 (7) B4 0.13 0.03 (10) C. temperature C1 0.29 0.06 (6) 0.22 C2 0.34 0.07 (5) C3 0.35 0.05 (4) 2.2. Taguchi Design of Experiment (1) Choose quality characteristics In order to measure the output quality and characteristics fro m desired value, Taguchi has utilized the Signal-to-Noise ratio; S/ N. S/N ratio also used to classify the results and evaluates them to determine the optimu m para meters. There are three S/N ratio’s characteristics; the nomina l the better, the smaller the better and the higher the better. Since this research is carried to reduce warpage, the smaller the better characteristic has been chosen and it is expressed as :          n i i y n NS 1 21 log10/ (1) yi represents the observation, n is the nu mber of tests in one trial. (2) Choose Control factor As shown Table 2, there are four factors identified to be the para meters in this research. They are the injection pressure (A), packing pressure (B), packing time (C),and the mold temperature (D).Taguchi method is used to analyze these four injection mo lding process parameters based on three-level design of e xperiments and orthogonal array L9(3 4 ) is created . The levels, factors and orthogonal array variance and the combination are shown in Table 3 respectively. Table 2 Selected Factors and Levels Factor Level 1 Level 2 Level 3 A. Injection Pressure 100 110 120 B. Packing pressure 65 75 85 C. Packing time 7 9 11 D. Mold temperature 80 90 100 Table 3 Combination of parameters in Orthogonal Array Variance A B C D 1 100 65 7 80 2 100 75 9 90 3 100 85 11 100 4 110 65 9 100 5 110 75 11 80 6 110 85 7 90 7 120 65 11 90 8 120 75 7 100 9 120 85 9 80 (3) Experimental data analysis After Molde x3D analysis, the results of the e xperiment to measure out the a mount of warpage calcu lated S/N ratio, calcu lated by Equation (1) S/ N rat io of each group, as shown in Table 4. In Table 4 can be obtained by injection mo lding of each factor on the table and the amount of warpage of the reaction the reaction diagra m, as shown in Table 5 and Figure 2. In smaller quality characteristics S / N rat io greater the better quality characteristics, according to tables and graphs can identify the best factor level co mbination A3B2C3D1, injection pressure 120Mpa, packing pressure 75MPa , packing time 11Sec, mold temperature 80℃. Table 4 Results of S/N and warpage of results A B C D Warpage S/N Advances in Technology Innovation , vol. 1, no. 2, 2016, pp. 46 - 49 48 Copyright © TAETI (mm) Ratio 1 1 1 1 1 0.0684 23.2989 2 1 2 2 2 0.0688 23.2482 3 1 3 3 3 0.0722 22.8293 4 2 1 2 3 0.0788 22.0695 5 2 2 3 1 0.0555 25.1141 6 2 3 1 2 0.0764 22.3381 7 3 1 3 2 0.0621 24.1382 8 3 2 1 3 0.0830 21.6184 9 3 3 2 1 0.0602 24.4081 Ave. 0.0694 23.2292 Table 5 The response table of S/N ratio A B C D Level 1 23.13 23.17 22.42 24.27 Level 2 23.17 23.33 23.24 23.24 Level 3 23.39 23.19 24.03 22.17 Effect 0.26 0.16 1.61 2.10 Rank 3 4 2 1 combination A3 B2 C3 D1 Fig. 2 S / N ratio reaction (4) Analysis of Variance (ANOVA) Analysis of variance (ANOVA, Analysis of Variance) is ma inly determined change of each factor on the quality characteristics variation effect, which is another way to find the most influential factor for the entire e xpe riment, in order to assess the experimental error. Table 6 initia l variance analysis results of the present experiment. Table 6 The first analysis of variance Factor SS DOF Variance A 0.1173 2 0.05866 B 0.0438 2 0.02189 C 3.8826 2 1.94131 D 6.6239 2 3.31196 Other 0.0000 0 0.000 Total 10.6676 8 1.33345 In Table 6 that the variance B factor holding pressure variation co mpared to the nu mber of other factors to low, so the integration of this factor to the error vector for a second analysis of variance. Table 7 shows D factor for the ent ire injection mo ld ing mo ld te mpe rature have a significant impact, accounting for 62.1% of the overall e xperiment, followed by C packing time and A. Injection pressure. In Table 5 choose the best combination A3B2C3D1 for mo ld flow analysis again to verify that the best combination of para meters, the optimu m a mount of warpage results about 0.0549 mm are shown in Table 8. Fig. 3 shows the simulation of best combination A3B2C3D1. Table 7 The second analysis of variance Factor SS DOF Var. F-Ratio Confi- dence ρ% A 0.117 2 0.0586 2.68 72.81% 1.09 B Pooled C 3.882 2 1.9413 88.69 99.99% 36.4 D 6.623 2 3.3119 151.3 99.99% 62.1 Error 0.043 2 0.0218 *At Least 99% Confidence Total 10.66 8 Table 8 best combination of parameters A B C D Warpage Combination 120 MPa 75 MPa 11 Sec 80 ℃ 0.0549 Fig. 3 Simulation of combination A3B2C3D1 3. Conclusions The paper used Taguchi quality engineering method to decide injection mo lding optimized combination factors. The results have shown that: (1) the best factor level co mbination A3B2C3D1, inject ion pressure 120Mpa, packing pressure 75MPa, packing time 11sec, mo ld temperature 80 ℃; (2) the entire injection mo lding mold te mperature have a significant impact, accounting for 62.1% of the overall e xperiment; and (3) the optimu m a mount of Advances in Technology Innovation , vol. 1, no. 2, 2016, pp. 46 - 49 49 Copyright © TAETI warpage results about 0.0549 mm. References [1] S. Ka ma ruddin, “Application of taguchi method in the optimization of inject ion mould ing para meters for manufacturing products from plastic blend,” International Journal of Engineering & Technology, vol. 2, no. 6, Dec. 2010. [2] N. A. Shuaib, “Warpage factors effectiveness of a thin shallow injection-molded part using Taguchi method,” International Journal of Engineering & Technology IJET-IJENS, vol. 11, no. 1, Feb. 2011. [3] H. Radhwan, M. T . Mustaffa, A. F. Annuar, H. Azmi, and M. Z. Zakaria, “An optimization of shrinkage in injection mo lding parts by using Taguchi method,” Journal of Advanced Research in Applied Mechanics, vol. 10, no. 1, pp. 1-8, 2015. [4] S. M . Nasir, K. A. Is mail, Z. Shayfull, and N. A. Shuaib, “ Co mparison between single and mu lti gates for min imization of wa rpage using Taguchi method in injection mold ing process for ABS material,” Key Engineering Materials, vol. 594-595, pp. 842-851, 2013. [5] S. Opasanon and P. Le rtsanti, “Impact analysis of logistics facility re location using the analytic hierarchy process (AHP),” International Transactions in Operational Research, pp. 325–339, 2013. [6] S. H. Kil, K. L. Dong, J. H. Kim, M. H. Li, and G. Neman “Utilizing the analytic hierarchy process to establish weighted values for evaluating the stability of slope revegetation based on hydroseeding applications in South Korea,” Sustainability, vol. 8, p. 58, 2016.