Full-range stress-strain curve estimation of aluminum alloys using machine learning-aided ultrasound

Abstract

Full-range stress-strain (SS) curves are crucial in understanding mechanical properties of a material such as the yield strength, ultimate tensile strength, and elongation. In this study, a full-range SS-curve was nondestructively estimated by applying machine learning to the ultrasonic amplitude-scan signal propagated through the material. The performance of the developed technique was validated using five-hundred aluminum alloy specimens with a wide spectrum of mechanical properties. The analyses of various ultrasonic properties, including nonlinearity and attenuation, with respect to the elements in the SS curves revealed how ultrasonics can be used to predict the SS curves without conventional destructive tensile testing. The proposed technique has significant potential for new applications in the fields of materials science and engineering, such as inline SS curve estimation during manufacturing.