贝叶斯极限梯度提升机结合粒子群算法的电阻点焊参数预测

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。

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

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.