Prediction of strength characteristics of high-entropy alloys Al-Cr-Nb-Ti-V-Zr systems

Abstract

Experimental evaluations of mechanical properties and investigations microstructure are time-intensive, requiring weeks or months to produce and characterize a small number of candidate alloys. In this work, machine learning approaches were used for prediction yield strengths of high-entropy alloys Al-Cr-Nb- Ti-V-Zr system at 20, 600 and 800 C. Surrogate prediction model was built with support vector regression algorithm by a dataset including more 30 alloys Al-Cr-Nb-Ti-V-Zr system. Four model alloys were fabricated for testing the surrogate model by vacuum arc melting. After that model alloys were annealed in a quartz tube at 1200 C 10 h