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Data-driven models of dynamic strength of resistance spot welds in high strength steels by regression and machine learning

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Abstract

Electric resistance spot welding technology has been widely applied to join sheet metal parts in the automobile industry for ensuring light weight and strong integrity. Many experiments have been carried out to measure the static and dynamic strength of spot welds for application to motor vehicles. The experimental data showed that the dynamic strength of spot welds is a function of loading rate, specimen thickness, spot weld nugget size, specimen type and steel grade. For the safe design of vehicle structures, it is important to accurately predict the weld strength as a function of those variables. Due to complexities in spot welding and failure, the existing regression models formulate the spot weld strength as a function of loading rate only and suffer from low prediction accuracy. Based on a large database of experimental tests on dynamic strength of spot welds for high strength steels, six data-driven models are developed using the state-of-the-art machine learning technology to quantify the dynamic strength of spot welds. Specifically, by use of artificial neural network (ANN) with built-in learning functions and optimized algorithms, the machine learning method can learn the data pattern from the training data set and accurately predict the dynamic strength as a multi-variable function of loading rate, specimen thickness, and weld nugget size. Moreover, both simple and more complex architectures of ANN models in conjunction with three typical activation functions (i.e., Sigmoid, Tanh and ReLU) are utilized to model the dynamic strength of spot welds. Six data-driven ANN models of dynamic strength are thus developed using the training data set and validated using the testing data set, and then compared with those obtained by the regression method. The advantages and disadvantages of those models are discussed accordingly.

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Acknowledgements

The authors are grateful to Professor Bill Chao in the University of South Carolina for his helpful discussions on the resistance spot weld tests.

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Authors and Affiliations

  1. Materials Technology, Savannah River National Laboratory, Aiken, SC, 29808, USA

    Xian-Kui Zhu

  2. Computer Science, Cornell Tech, New York, NY, 10044, USA

    Jesse B. Zhu

  3. Department of Materials Science and Engineering, The Ohio State University, Columbus, OH, 43221, USA

    Wei Zhang

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  1. Xian-Kui Zhu
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  2. Jesse B. Zhu
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  3. Wei Zhang
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Correspondence to Xian-Kui Zhu.

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Zhu, XK., Zhu, J.B. & Zhang, W. Data-driven models of dynamic strength of resistance spot welds in high strength steels by regression and machine learning. Multiscale and Multidiscip. Model. Exp. and Des. 5, 337–350 (2022). https://doi.org/10.1007/s41939-022-00123-y

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