游纬豪1,
徐海威2
1.福州大学数学与计算机科学学院 福州 350108
2.福建星云电子股份有限公司 福州 350000
基金项目:国家自然科学基金(61976055)
详细信息
作者简介:邓新国：男，1975年生，博士，副教授，硕士生导师，研究方向为智能算法、深度学习和增强学习等
游纬豪：男，1996年生，硕士生，研究方向为智能算法、深度学习和增强学习等
徐海威：男，1977年生，研究方向为自动化设备设计与集成
通讯作者:邓新国 xgdeng@fzu.edu.cn
中图分类号:TP39; TP399计量
文章访问数:397
HTML全文浏览量:166
PDF下载量:50
被引次数:0
出版历程
收稿日期:2020-05-08
修回日期:2020-09-08
网络出版日期:2020-09-16
刊出日期:2021-04-20
Prediction of Resistance Spot Welding Parameters by Bayes-XGBoost and Particle Swarm Optimization
Xinguo DENG1,,,Weihao YOU1,
Haiwei XU2
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)
摘要
摘要:电阻点焊是多种因素交互作用的复杂过程。该过程的复杂性加上数据规模小和工艺不稳定问题使得难以建立精确的数学模型来对电阻点焊参数进行预测。该文提出一种将贝叶斯极限梯度提升机(Bayes-XGBoost)与粒子群优化(PSO)算法结合的方法，对厚度为0.15 mm的镍片和0.4 mm的不锈钢电池正极帽选取合适的样本特征和样本组合；利用极限梯度提升机(XGBoost)的非线性切分能力和防控过拟合机制对点焊工艺参数进行正向训练，并引入贝叶斯优化为梯度提升机选取最佳超参数；利用粒子群优化算法的全局寻优能力，对可变目标值的工艺参数进行反向预测，从而得到最优工艺参数。电阻点焊实验表明该方法比文中其他对比算法具有较强的综合性能，能够有效辅助点焊工艺。
关键词:电阻点焊参数/
贝叶斯优化/
极限梯度提升机/
粒子群优化
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.
Key words:Resistance spot welding parameters/
Bayesian optimization/
eXtreme Gradient Boosting (XGBoost)/
Particle Swarm Optimization (PSO)
PDF全文下载地址:
https://jeit.ac.cn/article/exportPdf?id=6b238c48-0520-4ed0-b1a1-2cbf6a86b331
