A holistic inverse approach based on a multi-objective function optimisation model to recover elastic-plastic properties of materials from the depth-sensing indentation test

摘要

Recent years have seen an increased interest in the mechanical characterisation of materials via the inverse analysis of depth-sensing indentation data: however, at low-loads both the reaction forces measured by the instrument and the contact evolution at the indenter material interface may be severely affected by indentation size effects (ISEs). Notwithstanding the knowledge of ISE, the inverse analyses proposed to date have failed to investigate the divergence between the small-scale properties measured via indentation and the large-scale properties extracted from other techniques, e.g. tensile testing. Therefore, this study investigates the sensitivity of an inverse analysis methodology to the indentation size in relation to the size of the microstructure. The proposed inverse analysis approach is based on a multi-objective function (MOF) optimisation model that finds the combination of material properties (Young's modulus, yield stress and strain-hardening exponent) that provides the best fit to both the experimental load-displacement (P-h) curve extracted from the indentation instrument and pile-up profile of the residual imprint measured with an atomic force microscope. Therefore, the piling-up/sinking-in effect, which is strongly linked to the plastic hardening behaviour of the indented material, is considered to address the non-uniqueness issue of the inverse analysis of indentation. A Berkovich indenter was used to measure the near surface properties of three different materials, including a titanium alloy (Ti-6A1-4V), chromium-molybdenum-vanadium steel (CrMoV) and high purity copper (C110); materials have been selected to represent a wide range of ductile metallic materials so as to assess the generality of the MOF model. (C) 2019 Elsevier Ltd. All rights reserved.