A holistic inverse approach on depth-sensing indentation characterisation and its application for predicting residual stresses in multi-phase inertia friction welds - Iracheta-Cabrera, Omar Adrian (2017)
The present study is concerned with the development of an inverse analysis of the depth-sensing indentation test based on a multi-objective function (MOF) optimisation model. The input data of this model are the load-displacement (P-h) curve extracted from the indentation instrument and the surface topography of the residual imprint left by the indenter after the removal of the load measured via atomic force microscopy (AFM). A Swift’s power law material model was considered to represent the indented material and thus, the output of the optimisation are the Young’s modulus (E), yield stress (σy) and strain-hardening exponent (n). The optimisation problem was designed to minimise the error between both the experimental and predicted P-h curves, i.e. the first objective, and pile-up profiles, i.e. the second objective, with the aim of addressing the non-uniqueness of the inverse analysis of indentation. A 3D FE model of the depth-sensing indentation test has been developed in ABAQUS in order to generate the predicted data from a set of trial material properties, i.e. E, σy and n. The generation of FE input files (pre-processor) and extraction of FE output files (post-processor) have been automated through MATLAB and Python subroutines. The optimisation problem was solved by the trust-region reflective algorithm available in the MATLAB Optimization ToolboxTM and thus, concisely, the model minimised the experimental and predicted data by modifying iteratively the material properties, starting from the initial guess properties specified by the user, until convergence was reached. Upon convergence, the material properties were said to describe the elastic-plastic behaviour of the indented material.