Identification of stress-strain curves with yield plateau using indentation

Instrumented indentation technology has been recognized as an efficient method for determining the mechanical properties of metallic materials; however, it has been noted that existing research predominantly focuses on metals without yield plateau, which leaves a gap in understanding those that exhibit this behavior. In this work, a novel single indentation-based estimation technique is introduced, which synergistically combines the load-displacement curve with residual morphologies of indentations to accurately identify the tensile properties of metals with yield plateau. Through sensitivity analysis, the ratio of maximum pile-up height to maximum indentation depth and loading curvature is identified as key parameters for inverse estimation. The innovative approach employs artificial neural networks and optimization algorithms to establish a robust relationship between these parameters and tensile properties, significantly enhancing both prediction efficiency and accuracy. Validation across four different materials demonstrates the method's versatility and effectiveness, offering a groundbreaking approach for assessing the mechanical properties of materials exhibiting yield plateau.