Optimized design of composition and brazing process for Cu-Ag-Zn-Mn-Ni-Si-B-P alloy brazing material based on machine learning strategy to improve brazing properties

Abstract

As brazing devices become more sophisticated and service environments become more demanding, Cu-Ag-Zn-Mn-Ni-Si-B-P brazing material are subjected to higher wettability and brazed joint strength requirements. In this paper, a machine learning strategy is proposed. Firstly, the composition-process-properties dataset is obtained based on high-throughput experimental techniques. Then, the composition features are constructed by elemental chemical and physical parameters instead of the direct input of alloy composition, and the proposed novel feature selection method is used to effectively identify the key composition feature combinations that affect brazing properties. Then, the support vector regression algorithm was used to establish a joint strength model with an error of less than 3.47 % and a spreading area model with an error of less than 8.37 % using the key alloy composition features and brazing process features as inputs, respectively. Finally, two multi-objective optimization strategies, Bayesian optimization and NSGA-II genetic algorithm, were used for the integrated optimization design of composition and brazing process. The experimental results show that the Cu-14Ag-4Zn-24Mn-13.5Ni-0.4Si-0.3B-0.3 P alloy and the matched brazing process (945 °C and 12 min) were optimally designed using NSGA-II genetic algorithm, corresponding to the measured joint strength and spread area of 356 MPa and 412 mm2, respectively, which outperforms the high-throughput experimentally optimized data as well as commercialized data. The machine learning strategy established in this work provides a new research idea for the comprehensive improvement of brazing properties of brazing alloys with small data sets.