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Optimization of the plastic injection molding process using the Taguchi method, RSM, and hybrid GA-PSO

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Abstract

This paper proposes a systematic optimization model of process parameters in plastic injection molding (PIM). Firstly, the Taguchi method is employed for experimentation and data analysis, in which the quality characteristics for the plastic injection product are length and warpage. The control factors for the process are melt temperature, injection velocity, packing pressure, packing time, and cooling time. Moreover, the signal-to-noise (S/N) ratio and analysis of variance (ANOVA) are used to obtain a combination of parameter settings. Experimental data are set for the response surface methodology (RSM) in order to analyze and create two quality predictors and two S/N ratio predictors. The two quality predictors are associated with genetic algorithms (GA) to search for an optimal combination of process parameters that meets multiple-objective quality characteristics. Finally, four predictors are combined with the hybrid GA-PSO to find the final optimal combination of process parameters. The confirmation results show that the proposed model not only enhances the stability in the injection molding process, including the quality in length and warpage, but also reduces the costs of and time spent in the PIM process.

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Authors and Affiliations

  1. Department of Industrial Management, Chung Hua University, 707 Wu Fu Rd., Sec. 2, Hsinchu, 30012, Taiwan

    Wen-Chin Chen, Te-Ning Chen & Pei-Hao Tai

  2. Ph.D. Program of Technology Management, Chung Hua University, 707 Wu Fu Rd., Sec. 2, Hsinchu, 30012, Taiwan

    Manh-Hung Nguyen

  3. Department of Physical Education, National Hsinchu University of Education, 521 Nanda Rd., East Dist., Hsinchu, 30014, Taiwan

    Wen-Hsin Chiu

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  1. Wen-Chin Chen
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  2. Manh-Hung Nguyen
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  3. Wen-Hsin Chiu
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  4. Te-Ning Chen
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Correspondence to Pei-Hao Tai.

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Chen, WC., Nguyen, MH., Chiu, WH. et al. Optimization of the plastic injection molding process using the Taguchi method, RSM, and hybrid GA-PSO. Int J Adv Manuf Technol 83, 1873–1886 (2016). https://doi.org/10.1007/s00170-015-7683-0

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  • DOI: https://doi.org/10.1007/s00170-015-7683-0

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