Composition design of 7XXX aluminum alloys optimizing stress corrosion cracking resistance using machine learning

In this paper, three different strategies based on machine learning methods were applied to Al-Zn-Mg-Cu series alloy composition design with the targeted property of stress corrosion cracking (SCC) resistance. By comparing the results of the strategies, it was discovered that the performance of the efficient global optimization (EGO) method was better than that of response surface optimization method, and much better than that of Random method, among which the Al-6.05Zn-1.46Mg-1.32Cu-0.13Zr-0.02Ti-0.50Y-0.23Ce (named EGO alloy) alloy had the best stress corrosion cracking resistance. The slow strain rate test (SSRT) technique was carried out to compare the EGO alloy with the traditional 7N01 alloy. It indicated that the I-SCC of the new EGO alloy was lower than that of traditional 7N01 alloy for both single and double aging treatment. With the XRD, SEM and EDS analysis, it was found the rare earth elements formed Al8Cu4(Y, Ce) and quadrilateral phase Al20Ti2(Y, Ce) in the EGO alloy.