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Prediction of formation force during single-point incremental sheet metal forming using artificial intelligence techniques

Author

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  • Ali Alsamhan
  • Adham E Ragab
  • Abdulmajeed Dabwan
  • Mustafa M Nasr
  • Lotfi Hidri

Abstract

Single-point incremental forming (SPIF) is a technology that allows incremental manufacturing of complex parts from a flat sheet using simple tools; further, this technology is flexible and economical. Measuring the forming force using this technology helps in preventing failures, determining the optimal processes, and implementing on-line control. In this paper, an experimental study using SPIF is described. This study focuses on the influence of four different process parameters, namely, step size, tool diameter, sheet thickness, and feed rate, on the maximum forming force. For an efficient force predictive model based on an adaptive neuro-fuzzy inference system (ANFIS), an artificial neural network (ANN) and a regressions model were applied. The predicted forces exhibited relatively good agreement with the experimental results. The results indicate that the performance of the ANFIS model realizes the full potential of the ANN model.

Suggested Citation

  • 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.
    • Handle: RePEc:plo:pone00:0221341
    DOI: 10.1371/journal.pone.0221341
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    Cited by:

    1. 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.
    2. Shiori Gondo & Hirohiko Arai, 2022. "Effect and control of path parameters on thickness distribution of cylindrical cups formed via multi-pass conventional spinning," Journal of Intelligent Manufacturing, Springer, vol. 33(2), pages 617-635, February.

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