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Multi-objective optimization of process parameters in plastic injection molding using a differential sensitivity fusion method

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Abstract

The product quality, productivity, and cost are mainly considered to make the manufacturing plan in plastic injection molding (PIM). The process parameters in PIM play a crucial role in determining the product quality, productivity, and cost. There are actually contradictions between above three properties. Therefore, it is difficult to quickly and accurately obtain the process parameters setting that meet the product quality requirement under the premise of acceptable productivity and cost. In this paper, a differential sensitivity fusion method (DSFM) is proposed to perform the multi-objective optimization of process parameters in PIM for the product quality and productivity improvement and the cost-saving, which integrates sampling strategy, numerical simulation, metamodeling method, and multi-objective optimization algorithm. The sampling strategy is utilized to generate sampling points from the design space at different parameter levels. For the sampling points, the numerical simulation is implemented to calculate the objective responses. Based on the sampling points and their corresponding response, the metamodeling method is applied to construct the response predictors to calculate the objective responses for any sampling point in the global design space. The multi-objective optimization algorithm is executed to locate the Pareto-optimal solutions, where the response predictors are taken as the fitness functions. The automobile front bumper is taken as the case study to verify the proposed method. The numerical results demonstrate that the proposed metamodeling method has better prediction accuracy and performance compared to some classical methods (e.g., response surface model, Kriging) and the multiple objectives cannot reach the optimal simultaneously. Moreover, the trade-off analysis identifies the better solution for decision-making, which helps to quickly and effectively select the optimal process parameters setting.

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Funding

This work has been funded by the National Natural Science Foundation of China (51905476).

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Authors and Affiliations

  1. State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou, 310027, People’s Republic of China

    Huifang Zhou, Shuyou Zhang & Zili Wang

Authors
  1. Huifang Zhou
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  2. Shuyou Zhang
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  3. Zili Wang
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Contributions

Huifang Zhou’s contributions are conceptualization; writing—original draft; methodology; formal analysis; investigation; and project administration. Shuyou Zhang’s contributions are Investigation; resources; and validation. Zili Wang’s contributions are funding acquisition; project administration; methodology; formal analysis; supervision; visualization; and writing—review and editing.

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Correspondence to Zili Wang.

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Appendix

Appendix

The sampling points for constructing and training the response predictor are displayed in Table 11, and the objective responses of them are displayed in Table 12. The sampling points for confirmation or validation are displayed in Table 13, and the objective responses of them are displayed in Table 14.

Table 11 Sampling points for training generated by LHS
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Table 12 Numerical simulation results of responses for sampling points in Table 11
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Table 13 Sampling points for validation generated by LHS
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Table 14 Numerical simulation results of responses for sampling points in Table 13
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Zhou, H., Zhang, S. & Wang, Z. Multi-objective optimization of process parameters in plastic injection molding using a differential sensitivity fusion method. Int J Adv Manuf Technol 114, 423–449 (2021). https://doi.org/10.1007/s00170-021-06762-8

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  • DOI: https://doi.org/10.1007/s00170-021-06762-8

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