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Prediction of average surface roughness and formability in single point incremental forming using artificial neural network

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Abstract

Single point incremental forming (SPIF) is a flexible, innovative, and cheap process for rapid manufacturing of complex sheet metal parts. It is a crucial task for engineers to predict a process when many independent parameters are affecting simultaneously its performance. An artificial neural network (ANN) based prediction model was developed to evaluate average surface roughness (R a) and maximum forming angle (Ø max) while SPIF forming of AA5052-H32 material. A feedforward backpropagation network with Levenberg—Marquardt algorithm was employed to build ANN model. The ANNs (4-n-1, 4-n-2) were generated by introducing different combinations of transfer functions and a number of neurons. The confirmation runs were performed to verify the agreement between the ANN predicted and the experimental results. The developed ANN model (4-n-1) was capable of predicting the process response with an excellent accuracy and resulted in overall R-value, MSE, and MAPE of 0.99807, 0.0209, and 5.96% for R a 0.99913, 0.0281, and 0.003 for Ø max. The optimum 4-n-2 model was built with overall R-value, MSE of 0.99999 and 0.057194, respectively. Hence, it was found that the engineering efforts may be reduced in the SPIF process with successful ANN model implementation.

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Authors and Affiliations

  1. Mechanical Engineering Department, Indian Institute of Technology, Bombay, 400076, India

    Amrut Mulay

  2. Mechanical Engineering Department, National Institute of Technology, Warangal, 506004, India

    B. Satish Ben & Syed Ismail

  3. Faculty of Management and Computer Modeling, Kielce University of Technology, Kielce, 25-314, Poland

    Andrzej Kocanda

Authors
  1. Amrut Mulay
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  2. B. Satish Ben
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  3. Syed Ismail
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  4. Andrzej Kocanda
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Correspondence to Amrut Mulay.

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Mulay, A., Ben, B.S., Ismail, S. et al. Prediction of average surface roughness and formability in single point incremental forming using artificial neural network. Archiv.Civ.Mech.Eng 19, 1135–1149 (2019). https://doi.org/10.1016/j.acme.2019.06.004

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  • DOI: https://doi.org/10.1016/j.acme.2019.06.004

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