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Performance analysis and comparison of machine learning algorithms for predicting nugget width of resistance spot welding joints

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Abstract

Recently, machine learning algorithms have been extensively utilized in resistance spot welding (RSW) applications to develop non-destructive weldability assessment systems to predict nugget width of RSW joints. However, different predictive models have different prediction performance that can be highly inconsistent. It is critical to compare predictive models and determine the efficient model(s). To the best of our knowledge, a comprehensive analysis and systematic prediction performance comparison of RSW nugget width prediction models have not been performed. This paper presents a statistical performance comparison methodology based on bootstrapping and hypothesis testing techniques to systematically compare the prediction performance of predictive models and determine the efficient model(s). Also, a deep neural net (DNN) nugget width prediction model is developed, analyzed, and compared with prior models. Bootstrapping is applied to generate sampling distributions for each predictive model, and statistical comparison tests are employed to analyze and compare the performance of each predictive model and identify statistically significant performance differences. Results of this analysis indicate that DNN, developed for RSW nugget width prediction in this paper, outperforms previous models.

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Funding

This research is based upon work supported by the Digital Manufacturing and Design Innovation Institute (DMDII) under grant DMDII-15-07-04.

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Authors and Affiliations

  1. Department of Industrial and Systems Engineering, Wayne State University, 4815 Fourth Street, Rm. 1062, Detroit, MI, 48202, USA

    Saeed Zamanzad Gavidel & Jeremy L. Rickli

  2. Department of Computer Science, Big Data Research Laboratory, Wayne State University, 5057 Woodward Avenue, Detroit, MI, 48202, USA

    Saeed Zamanzad Gavidel & Shiyong Lu

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  1. Saeed Zamanzad Gavidel
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Correspondence to Saeed Zamanzad Gavidel.

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Zamanzad Gavidel, S., Lu, S. & Rickli, J.L. Performance analysis and comparison of machine learning algorithms for predicting nugget width of resistance spot welding joints. Int J Adv Manuf Technol 105, 3779–3796 (2019). https://doi.org/10.1007/s00170-019-03821-z

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