Prediction of Resistance Spot Welding Parameters by Bayes-XGBoost and Particle Swarm Optimization

Xinguo DENG; Weihao YOU; Haiwei XU

doi:10.11999/JEIT200353

Volume 43 Issue 4

Apr. 2021

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Xinguo DENG, Weihao YOU, Haiwei XU. Prediction of Resistance Spot Welding Parameters by Bayes-XGBoost and Particle Swarm Optimization[J]. Journal of Electronics & Information Technology, 2021, 43(4): 1042-1049. doi: 10.11999/JEIT200353

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Xinguo DENG, Weihao YOU, Haiwei XU. Prediction of Resistance Spot Welding Parameters by Bayes-XGBoost and Particle Swarm Optimization[J]. Journal of Electronics & Information Technology, 2021, 43(4): 1042-1049. doi: 10.11999/JEIT200353

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Prediction of Resistance Spot Welding Parameters by Bayes-XGBoost and Particle Swarm Optimization

doi: 10.11999/JEIT200353

1.
College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350108, China
2.
Fujian Nebula Electronics Co. LTD, Fuzhou 350000, China

Funds: The National Natural Science Foundation of China (61976055)

Received Date: 2020-05-08
Rev Recd Date: 2020-09-08

Available Online: 2020-09-16

Publish Date: 2021-04-20

Abstract

Abstract

Resistance spot welding is a complex process in which many factors interact. Given the small size of data sets available and the complex nature of unstable processes, it is difficult to establish an accurate mathematical model to predict the parameters of resistance spot welding. An optimal computing method for solving this problem is presented. The method combines Bayes-XGBoost with the Particle Swarm Optimization (PSO) algorithm to select suitable features and to enable the optimal combinations of samples for 0.15 mm nickel sheets and for 0.4 mm stainless steel battery positive caps; The non-linear slicing ability and anti-overfitting mechanism of eXtreme Gradient Boosting (XGBoost) are used to train forward spot welding parameters; and Bayesian optimization is applied to the XGBoost's optimal parameter selection. The method uses the global optimization feature of Particle Swarm Optimization (PSO) to predict the backward process parameters with variable target values such that the optimal process parameters are obtained. Compared with other algorithms mentioned in this paper, this method offers more comprehensive performance and possesses better capabilities to effectively assist in the spot welding process, which are demonstrated by the resistance spot welding experiments performed.
- Resistance spot welding parameters,
- Bayesian optimization,
- eXtreme Gradient Boosting (XGBoost),
- Particle Swarm Optimization (PSO)

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References(23)

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