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Generative machine learning-based multi-objective process parameter optimization towards energy and quality of injection molding

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Abstract

The high energy intensity and rigorous quality demand of injection molding have received significant interest under the background of the soaring production of global plastic industry. As multiple parts can be produced in a multi-cavity mold during one operation cycle, the weight differences of these parts have been demonstrated to reflect their quality performance. In this regard, this study incorporated this fact and developed a generative machine learning-based multi-objective optimization model. Such model can predict the qualification of parts produced under different processing variables and further optimize processing variables of injection molding for minimal energy consumption and weight difference amongst parts in one cycle. Statistical assessment via F1-score and R2 was performed to evaluate the performance of the algorithm. In addition, to validate the effectiveness of our model, we conducted physical experiments to measure the energy profile and weight difference under varying parameter settings. Permutation-based mean square error reduction was adopted to specify the importance of parameters affecting energy consumption and quality of injection molded parts. Optimization results indicated that the processing parameters optimization could reduce ~ 8% energy consumption and ~ 2% weight difference compared with the average operation practices. Maximum speed and first-stage speed were identified as the dominating factors affecting quality performance and energy consumption, respectively. This study could contribute to the quality assurance of injection molded parts and facilitate energy efficient and sustainable plastic manufacturing.

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Availability of data and materials

The datasets used or analyzed during the current study are available from the corresponding author on reasonable request.

Funding

This study received the financial support and sponsorship from the Project of Guangdong Science and Technology Innovation Strategy (Grant number STKJ2021177 and STKJ202209065) and STU Scientific Research Foundation for Talents (NTF20019).

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Authors and Affiliations

  1. Department of Mechanical Engineering, Shantou University, Shantou, 515063, China

    Yirun Wu, Yiqing Feng, Shitong Peng & Zhongfa Mao

  2. Shantou Ruixiang Mould Co., Ltd., Shantou, 515041, China

    Bin Chen

Authors
  1. Yirun Wu
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  2. Yiqing Feng
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  3. Shitong Peng
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  4. Zhongfa Mao
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  5. Bin Chen
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Contributions

Yirun Wu: writing—original draft, visualization, formal analysis, investigation, writing—reviewing and editing. Yiqing Feng: conceptualization, methodology, software, data curation. Shitong Peng: supervision, project administration. Zhongfa Mao: methodology, software, project administration. All authors read and approved the final manuscript.

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Correspondence to Shitong Peng.

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Wu, Y., Feng, Y., Peng, S. et al. Generative machine learning-based multi-objective process parameter optimization towards energy and quality of injection molding. Environ Sci Pollut Res 30, 51518–51530 (2023). https://doi.org/10.1007/s11356-023-26007-3

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