Abstract: Based on the welding process information,the machine learning model quality prediction method is the main way to realize the reliable assessment of the performance of welded joints in power lithium battery packs.To address the issues of unreasonable hyperparameter selection and poor interpretability in traditional machine learning models,a process information dataset for lithium battery resistance spot welding was established.A machine learning model for joint performance prediction was constructed,and the prediction performances of different machine learning models on a small dataset of resistance spot welding were compared and analyzed.The NSGA-Ⅲ-EBM model was proposed based on the third-generation Non-dominated Sorted Genetic Algorithm (NSGA-Ⅲ).The generalizability of the NSGA-Ⅲ-EBM model to different feature data was investigated,and the input features were analyzed both globally and locally.The results indicated that,for predicting the welded joint's kernel diameter and tensile shear load,the EBM model outperformed the MLP,MLS-SVR,and XGBoost models,achieving an average RMSE and R2 of 2.4127 and 0.8466,respectively on the test set.After hyperparameter optimization using NSGA-Ⅲ,the NSGA-Ⅲ-EBM model improved the average RMSE and R2 on the test set by 17.2% and 2.1%,respectively,compared to the unoptimized EBM model.Additionally,important features that affected joint performance were identified,providing a basis for the dynamic adjustment of welding process parameters.

Key words: resistance spot welding, joint performance, interpretable model, hyperparameter optimization

摘要： 基于焊接过程信息和机器学习模型的质量预测方法,是实现动力锂电池组焊接接头性能可靠评估的主要途径。为解决传统机器学习模型存在的超参数选择不合理和预测结果可解释性差等问题,建立了锂电池电阻点焊过程信息数据集,构建了接头性能预测机器学习模型,对比分析了不同机器学习模型对电阻点焊小样本数据集的预测性能;基于第三代非支配排序遗传算法(NSGA-Ⅲ)提出了NSGA-Ⅲ-EBM模型,研究了NSGA-Ⅲ-EBM模型对不同特征数据的泛化性,并对输入特征进行了全局解释和局部解释分析。结果表明,针对焊接接头的熔核直径以及拉伸剪切载荷的预测,EBM模型相较于MLP、MLS-SVR和XGBoost模型具有更好的预测性能,在测试集上的平均RMSE、R2分别为2.4127、0.8466;采用NSGA-Ⅲ进行超参数优化后的NSGA-Ⅲ-EBM模相较于未优化的EBM模型,在测试集上的平均RMSE和R2分别提升了17.2%、2.1%;此外,还确定了影响接头性能的重要特征,为焊接工艺参数的动态调整提供了依据。

关键词: 电阻点焊, 接头性能, 可解释模型, 超参数优化