机器学习辅助的无铅焊料合金力学性能预测及合金设计

Machine learning of the mechanical properties and data-driven design of lead-free solder alloys

研发具有高强度、高韧性、耐蠕变、耐热疲劳、可焊性好的无铅焊料合金对现代电子工业的发展具有重要意义.由于强度和塑性之间的内在矛盾关系,提高焊锡合金强度的同时就有可能降低其塑性,导致其韧性降低.同时优化焊料合金两种相互制约的性能,从而实现其综合力学性能的提升具有较大的挑战性.本文针对无铅焊锡合金分别建立了以合金成分和原子参数为特征的两类机器学习模型,通过精度对比,选择以原子特征构建的两个预报精度最高的机器学习模型作为最终的学习器,预测了无铅焊料合金的抗拉强度和断裂延伸率.根据各元素的成分范围和变化步长构造了虚拟样本集,采用特征SHAP值对特征取值进行约束,筛选出候选虚拟样本并进行预报,以强度和塑性的L2范数作为综合力学性能指标,选取在帕累托边界上综合力学性能最高的两个虚拟样本进行实验验证.结果表明通过机器学习设计出的两种合金均实现了综合力学性能的提升.本研究可为复杂合金进行多目标优化设计提供参考.

The development of lead-free solder alloys with high-strength,high-toughness,good creep,thermal-fatigue resistance,and good soldering properties are essential for the industrial development of modern electronics and microelectronics.Improving the strength of solder alloys usually induces a reduction in ductility due to the tradeoff between strength and ductility,which ultimately results in deterioration in toughness.However,it is challenging to optimize these two mutually restrictive properties of solder alloys simultaneously while comprehensively improving the mechanical properties.In this study,two kinds of machine learning models were established for Sn-based solder alloys.The alloy composition and atomic scale parameters were used as input features,respectively,and tensile strength and fracture elongation were the target properties.Two machine learning models with atomic features exhibited high tensile strength and fracture elongation prediction accuracies,respectively.A virtual sample space was constructed for Sn-based alloys according to the range and variation step size of the alloying elements in the original training dataset,and the average absolute SHAP values were used to rank and select features.With the L2norm of strength and ductility as an index of the comprehensive mechanical property,two virtual samples on the Pareto front were selected for experimental verification.The experimental results were highly consistent with the ML predictions,and the selected samples exhibited improved properties in terms of their comprehensive mechanical properties.The results of this study can be used as guidance for the multi-objective optimization design of complex alloys.