Prediction of Resistance Spot Welding Parameters by Bayes-XGBoost and Particle Swarm Optimization
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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。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.
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表 1 Bayes-XGBoost+粒子群算法
输入: 工艺参数及结果值、需求值 输出: 预测的工艺参数 步骤1 读入训练数据，对数据做归一化和切分。 步骤2 初始化参数集合，使用贝叶斯优化搜寻XGBoost的参数。 步骤2.1 输入参数到高斯模型，输入样本到XGBoost模型； 步骤2.2 XGBoost进行最优化分裂，直到满足停止条件； 步骤2.3 若满足误差要求则停止迭代，否则通过提取函数选
择参数进行步骤2.1。步骤3 初始化粒子群，设定目标函数，进行粒子群寻优。 步骤3.1 计算微粒适应度值。 步骤3.2 计算个体和群体的历史最优位置、最优适应度值。 步骤3.3 更新微粒速度和位置，若满足终止条件则停止迭
代，否则进行步骤3.1。
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表 2 参数范围
参数 焊接压力(N) 焊接电压1(V) 焊接电压2(V) 焊接时间1(ms) 焊接时间2(ms) 范围 20, 23, 25, 27, 35 2.2, 2.3, 2.5, 3.0 2.3, 2.5, 2.7, 3.2 1, 2, 3, 4 1, 2, 3, 4
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表 3 XGBoost参数集合
名称 意义 范围 Eta 特征权重缩减系数 (0.01, 0.3) Max_depth 最大树深 (2, 16) Min_child_weight 最小叶子权重和 (0.1, 10) Lambda L1 正则化项 (0,10) Alpha L2 正则化项 (0, 10) Gamma 最小损失函数下降值 (0, 20) Sample 特征采样比例 (0.5, 1.0)
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表 5 模型预测结果
算法 TRAIN-MAE TEST-MAE RF 0.0345 0.0389 BP 0.0291 0.0381 Grid-XGBoost 0.0416 0.0314 Bayes-XGBoost 0.0232 0.0151
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表 6 算法的误差
算法 RMSE TIC RF+粒子群 4.3125 0.0351 BP+粒子群 6.7281 0.0495 Grid-XGBoost+粒子群 2.7634 0.0166 Bayes-XGBoost+粒子群 1.8355 0.0109
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表 8 算法所用时间
算法 时间 RF+粒子群 3 min 09 s BP+粒子群 1 min 53 s Grid-XGBoost+粒子群 1 min 36 s Bayes-XGBoost+粒子群 1 min 12 s
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