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Prediction and optimization of depth of penetration for stainless steel gas tungsten arc welded plates using artificial neural networks and simulated annealing algorithm

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Abstract

The quality of a weld joint is highly influenced by depth of penetration. Hence, accurate prediction and maximization of depth of penetration is highly essential to ensure a good-quality joint. This paper highlights the development of neural network model for predicting depth of penetration and optimizing the process parameters for maximizing depth of penetration using simulated annealing algorithm. The process parameters chosen for the study are welding current, welding speed, gas flow rate and welding gun angle. The chosen output parameter was depth of penetration. The experiments were conducted based on design of experiments using fractional factorial with 125 runs. Using the experimental data, feed-forward backpropagation neural network model was developed and trained using Levenberg–Marquardt algorithm. It was found that ANN model based on network 4-15-1 predicted depth of penetration more accurately. A mathematical model was also developed correlating the process parameters with depth of penetration for doing optimization. A source code was developed in MATLAB to do the optimization. The optimized process parameters gave a value of 3.778 mm for depth of penetration.

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Authors and Affiliations

  1. Kumaraguru College of Technology, Coimbatore, 641049, India

    R. Sudhakaran & M. Balaji

  2. Sree Sakthi Engineering College, Coimbatore, India

    V. Vel Murugan

  3. SASTRA University, Thanjavur, India

    P. S. Sivasakthivel

Authors
  1. R. Sudhakaran
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  2. V. Vel Murugan
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  3. P. S. Sivasakthivel
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  4. M. Balaji
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Correspondence to R. Sudhakaran.

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Sudhakaran, R., Vel Murugan, V., Sivasakthivel, P.S. et al. Prediction and optimization of depth of penetration for stainless steel gas tungsten arc welded plates using artificial neural networks and simulated annealing algorithm. Neural Comput & Applic 22, 637–649 (2013). https://doi.org/10.1007/s00521-011-0720-5

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  • DOI: https://doi.org/10.1007/s00521-011-0720-5

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