Data-driven estimation of plastic properties of alloys using neighboring indentation test

Abstract

A data-driven estimation method for the plastic properties of alloys was proposed using indentations at neighboring positions. An instrumented indentation test is an efficient approach to measure mechanical properties such as equivalent elastic modulus and hardness. In the mechanical test, subsequent experiments are generally performed apart from existing indentations to avoid the interaction effect caused by the dependency on the deformation history of metal plasticity. In this study, the interaction effect was utilized to estimate the plastic properties, based on the difference of load-depth curves between the first and second indentations at neighboring positions. Using finite element simulations of the neighboring indentation tests, the effective experimental conditions were examined, and response surfaces of the loading curvatures were characterized to determine two material constants of a simple constitutive model of plasticity. Finally, the proposed approach was validated for application to aluminum alloys and stainless steel. It can be also applied to various alloys characterized by different elastic moduli. KEYWORDS elastic-plastic material; finite element method; mechanical testing; response surface; instrumented indentation test; material database; ARTICLE CLASSIFICATION: Data analysis (AI, machine learning, data-driven analysis, descriptor development, structure search/identification)