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Data-driven analysis in magnetic field-assisted electrical discharge machining of high-volume SiCp/Al

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Abstract

This paper presents a framework of data-driven intelligence system which can be applied on magnetic field-assisted electrical discharge machining (MF-EDM) machining process for SiC particulate reinforced Al-based metal matrix composites (SiCp/Al) with different high-volume fractions. The implemented system consists of data modelling, predicating, optimization and monitoring modules. A multi-objective moths search (MOMS) optimization algorithm with backpropagation neural network (BPNN) model and multi-hierarchy non-dominated strategy is proposed for tuning optimal processing performance. Data are collected from machining different fraction volumes of SiCp/Al composites by MF-EDM, with peak current, magnetic, pulse width and pulse interval time as input, and material removal rate, electrode wear rate, surface roughness as output. The BPNN model shows the best accuracy compared to K-nearest neighbors, least square support vector machine and Kriging model. To demonstrate the effectiveness of the MOMS optimization algorithm, a set of results is selected as paradigm, which dominates 95.83% original experiments. A verification experiment is also done for an optimized parameter with 65% fraction and 0.2T magnetic. Both result data and three-dimensional surface topography comparison show that the verification experiment result dominates the original experiment of similar input designs.

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Acknowledgements

The authors would like to thank Mr. Haishen Yu and Mr. Liquan Lin for preparing the experiments.

Funding

This research is supported by National Key R&D Program of China (No. 2020YFB2008203), Project named “Mold design, manufacturing and high-efficiency precision molding of small module gear”.

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Authors and Affiliations

  1. School of Aerospace Engineering, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan, 430074, Hubei, China

    Tao Xue, Long Chen, Zhen Zhang, Jiaquan Zhao & Yi Zhang

  2. School of Materials Science and Engineering, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan, 430074, Hubei, China

    Dongxu Wen & Huachang Wang

Authors
  1. Tao Xue
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  2. Long Chen
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  3. Zhen Zhang
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  4. Jiaquan Zhao
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  5. Yi Zhang
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  7. Huachang Wang
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Contributions

Tao Xue: conceptualization, methodology and writing; Long Chen: software and validation; Jiaquan Zhao: design and writing. Zhen Zhang: experiment and analysis; Yi Zhang: experiments; Dongxu Wen: funding and resources, Huachang Wang: funding and resources.

Corresponding authors

Correspondence to Zhen Zhang or Jiaquan Zhao.

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Xue, T., Chen, L., Zhang, Z. et al. Data-driven analysis in magnetic field-assisted electrical discharge machining of high-volume SiCp/Al. Int J Adv Manuf Technol 122, 2775–2791 (2022). https://doi.org/10.1007/s00170-022-09940-4

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  • DOI: https://doi.org/10.1007/s00170-022-09940-4

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