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Data-driven method for process optimization in electromagnetic-electrohydraulic hybrid high-velocity sheet metal forming

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Abstract

High-velocity forming (HVF) is a flexible process that can essentially enhance the forming limit of lots of low-ductility metals; however, its complicated mechanism for the deformation control hinders its wide applications. To this end, this paper introduces a data-driven methodology to uncover the nonlinear regulation of the deformation control and figure out the optimum process setting toward the preferred deformation behavior. More specifically, we adopt a newly electromagnetic-electrohydraulic hybrid forming process as the research object. Artificial neural networks are adopted to learn the process mapping between three discharge parameters (electromagnetic voltage, electrohydraulic voltage, and discharge timing) and two forming indexes (forming height and thinning rate). And then, the genetic-algorithm-based multi-objective method is used to maximize forming height, meanwhile, minimize thinning rate, and obtain the optimum process parameters. Based on such optimization, two deformation control rules are identified: (1) the greater electromagnetic voltage is preferred for better deformation uniformity; (2) the optimum discharging timing and electrohydraulic voltage are negatively correlated with each other. Based on the optimum parameter setting, the experimentations are conducted to validate such results. A 50% improvement rate on the limiting forming height is observed compared with the conventional electrohydraulic process, which is about 150% higher than the improvement rate (20.9%) obtained in our previous work, showing a substantial process improvement. In a word, the proposed method provides an effective approach for better understanding and exploiting the complicated process control mechanisms in the high-velocity forming process and its variants.

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Funding

This work was supported by the National Natural Science Foundation of China (52107150, 52077092, 51821005, and 51877122) and the Fundamental Research Funds for the Central Universities (HUST: 2020kfyXJJS055).

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Authors and Affiliations

  1. Wuhan National High Magnetic Field Center, Huazhong University of Science and Technology, Wuhan, 430074, China

    Ziye Wang, Zhipeng Lai, Changxing Li, Chen Wang, Yu Zheng, Wei Xu, Meng Li, Yuhang Gao, Zixuan Zhang, Quanliang Cao, Xiaotao Han & Liang Li

  2. State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China

    Ziye Wang, Zhipeng Lai, Changxing Li, Chen Wang, Yu Zheng, Wei Xu, Meng Li, Yuhang Gao, Zixuan Zhang, Quanliang Cao, Xiaotao Han & Liang Li

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  1. Ziye Wang
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  2. Zhipeng Lai
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  3. Changxing Li
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  4. Chen Wang
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  9. Zixuan Zhang
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  12. Liang Li
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Contributions

All authors contributed to the study’s conception and design. Material preparation, data collection, and analysis were performed by Ziye Wang, Zhipeng Lai, and Changxing Li. The first draft of the manuscript was written by Ziye Wang and Zhipeng Lai, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Zhipeng Lai or Liang Li.

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Wang, Z., Lai, Z., Li, C. et al. Data-driven method for process optimization in electromagnetic-electrohydraulic hybrid high-velocity sheet metal forming. Int J Adv Manuf Technol 121, 4355–4365 (2022). https://doi.org/10.1007/s00170-022-09621-2

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  • DOI: https://doi.org/10.1007/s00170-022-09621-2

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