波动工况下点焊质量在线预测及模型解释

基于电阻点焊过程的多传感信号特征，面向多种板材组合建立焊点质量在线预测模型，研究了异常工况波动对四类机器学习回归模型的影响，分析了不同模型和输入变量对含异常工况试验数据集的适应性，并采用Shapley值、t-SNE等方法对波动工况下的模型性能进行解释.结果表明，高斯过程回归模型和电阻 + 力信号具有最佳的熔核直径预测性能，焊接电流、热输入能量和电极位移峰值特征对于波动工况具有良好普适性.此外，异常工况引起的信号特征分布差异会显著影响回归预测模型的泛化性能，应尽量减少训练集与数据集差异以提高焊点质量预测的准确性.

Online prediction of resistance spot weld quality and model explanation under fluctuating conditions

Based on the features of multi-sensing signals in resistance spot welding process, online prediction models were established for the spot weld quality of different stack-ups in this paper. The influence of fluctuating welding conditions fluctuation on four machine learning regression models was studied, and the adaptability of different models and input variables on the database containing data of abnormal conditions was analyzed. Shapley value, and t-SNE methods were used to explain the model performance under fluctuating conditions. The results show that the Gaussian process regression model and resistance + force signal input had the best prediction performance of nugget diameter. Features of welding current, heat input and peak value of electrode displacement had good universality under fluctuating conditions. Besides, the difference of feature distribution caused by condition fluctuation could significantly influence the generalization performance of regression models. Thereby, the reduction of the difference between training set and test set could improve the prediction accuracy.