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Predicting the evolution of sheet metal surface scratching by the technique of artificial intelligence

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Abstract

This paper presents an artificial intelligence (AI) method for the evolution prediction of surface scratching in sheet metals subjected to contact sliding. Ball-on-disk sliding was employed, and ball diameter, normal load, surface roughness, sliding cycles and the maximum scratching depth in the metal sheet were taken as the fuzzy variables to assess the contributions of individual variables to the surface damage. To improve the prediction accuracy, the quantum-behaved particle swarm optimisation (QPSO) algorithm was further developed and utilised to refine the fuzzy model by optimising the membership functions of the fuzzy variables. It was found that this AI technique, which integrates the fuzzy set theory with the improved QPSO algorithm, can accurately, reliably and efficiently predict the surface scratching evolution, which is otherwise impossible to be implemented.

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Data availability

Queries about data and materials should be addressed to L.Z (zhanglc@sustech.edu.cn).

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Funding

The study is financially supported by the Baosteel Australia Research and Development Centre under Project BA17001 and the ARC Research Hub under Project IH140100035. Liangchi Zhang appreciates the support of the Guangdong Specific Discipline Project (2020ZDZX2006). The first author is financially supported by China CSC and UNSW TFS scholarships.

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Authors and Affiliations

  1. School of Mechanical and Manufacturing Engineering, The University of New South Wales, Sydney, NSW, 2052, Australia

    Wei Li & Chuhan Wu

  2. Department of Mechanics and Aerospace Engineering, Southern University of Science and Technology, Shenzhen, 518055, Guangdong, China

    Liangchi Zhang

  3. Baoshan Iron & Steel Co., Ltd., Shanghai, 200941, China

    Xinping Chen, Zhenxiang Cui & Chao Niu

Authors
  1. Wei Li
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  2. Liangchi Zhang
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  3. Xinping Chen
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  4. Chuhan Wu
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  5. Zhenxiang Cui
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  6. Chao Niu
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Contributions

L.Z. initiated and supervised the project. W.L. carried out the experiments, established and optimised the fuzzy prediction model under the supervision of C.W. and L.Z. W.L. prepared the manuscript draft; C.W. and L.Z. revised the manuscript and contributed to the discussions. X.C., Z.C. and C.N. provided the experiment materials and made helpful suggestions on the experiment planning. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Liangchi Zhang.

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Li, W., Zhang, L., Chen, X. et al. Predicting the evolution of sheet metal surface scratching by the technique of artificial intelligence. Int J Adv Manuf Technol 112, 853–865 (2021). https://doi.org/10.1007/s00170-020-06394-4

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  • DOI: https://doi.org/10.1007/s00170-020-06394-4

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