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Application of artificial intelligence techniques in incremental forming: a state-of-the-art review

Author

Listed:
  • Aniket Nagargoje

    (PDPM Indian Institute of Information Technology, Design and Manufacturing)

  • Pavan Kumar Kankar

    (Indian Institute of Technology Indore)

  • Prashant Kumar Jain

    (PDPM Indian Institute of Information Technology, Design and Manufacturing)

  • Puneet Tandon

    (PDPM Indian Institute of Information Technology, Design and Manufacturing)

Abstract

Incremental forming (IF) is one of the novel manufacturing processes that has gained much attention from researchers and practitioners. As a result, various analytical and numerical models of IF have been developed. The remarkable thing is that artificial intelligence (AI)-based computational methods have been used in solving IF-related problems. This study reviews the extant literature relevant to IF. It is found that AI techniques such as artificial neural networks, support vector regression, decision trees, fuzzy logic, genetic algorithms, particle swarm optimization have been used in solving IF-relevant problems. In addition, hybrid methods that combine some of the above-mentioned techniques have also been used. Moreover, it is shown that the performance parameters of IF such as springback and geometrical accuracy, formability, forming forces, surface roughness, forming time, and average deformed sheet thickness have been predicted and a few toolpath strategies have been developed using AI-based techniques. Thus, this study would serve researchers and practitioners who want to solve IF-related problems and advance the applicability of IF.

Suggested Citation

  • Aniket Nagargoje & Pavan Kumar Kankar & Prashant Kumar Jain & Puneet Tandon, 2023. "Application of artificial intelligence techniques in incremental forming: a state-of-the-art review," Journal of Intelligent Manufacturing, Springer, vol. 34(3), pages 985-1002, March.
    • Handle: RePEc:spr:joinma:v:34:y:2023:i:3:d:10.1007_s10845-021-01868-y
    DOI: 10.1007/s10845-021-01868-y
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    References listed on IDEAS

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    1. Xin Tong & Qiang Liu & Shiwei Pi & Yao Xiao, 2020. "Real-time machining data application and service based on IMT digital twin," Journal of Intelligent Manufacturing, Springer, vol. 31(5), pages 1113-1132, June.
    2. Juncheng Wang & Bin Zou & Mingfang Liu & Yishang Li & Hongjian Ding & Kai Xue, 2021. "Milling force prediction model based on transfer learning and neural network," Journal of Intelligent Manufacturing, Springer, vol. 32(4), pages 947-956, April.
    3. Hietam Elhoone & Tianyang Zhang & Mohd Anwar & Salil Desai, 2020. "Cyber-based design for additive manufacturing using artificial neural networks for Industry 4.0," International Journal of Production Research, Taylor & Francis Journals, vol. 58(9), pages 2841-2861, May.
    4. Shubham Vaishnav & Ankit Agarwal & K. A. Desai, 2020. "Machine learning-based instantaneous cutting force model for end milling operation," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1353-1366, August.
    5. Christoph Hartmann & Daniel Opritescu & Wolfram Volk, 2019. "An artificial neural network approach for tool path generation in incremental sheet metal free-forming," Journal of Intelligent Manufacturing, Springer, vol. 30(2), pages 757-770, February.
    6. Ivanna Baturynska & Kristian Martinsen, 2021. "Prediction of geometry deviations in additive manufactured parts: comparison of linear regression with machine learning algorithms," Journal of Intelligent Manufacturing, Springer, vol. 32(1), pages 179-200, January.
    7. Ali Alsamhan & Adham E Ragab & Abdulmajeed Dabwan & Mustafa M Nasr & Lotfi Hidri, 2019. "Prediction of formation force during single-point incremental sheet metal forming using artificial intelligence techniques," PLOS ONE, Public Library of Science, vol. 14(8), pages 1-18, August.
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