Identification of elastic-plastic material parameters from pyramidal indentation of thin films

TLDR: In this article, the authors proposed a neural network-based method for the determination of the reduced modulus as well as the nonlinear hardening behavior of both the film and substrate materials.

Abstract: The indentation experiment is a popular method for the investigation of mechanical properties of thin films. By application of conventional methods, the hardness and the stiffness of the film material can be determined by limiting the indentation depth to well below the film thickness so that the substrate effects can be eliminated. In this work a new method is proposed, which allows for a determination of the reduced modulus as well as the nonlinear hardening behaviour of both the film and substrate materials. To this end, comparable deep indentations are made on the film/substrate composite to obtain sufficient information on the mechanical properties of both materials. The inverse problem is solved by training neural networks on the basis of finite–element simulations using only the easily measurable hardness and stiffness behaviour as input data. It is shown that the neural networks are very robust against noise in the load and depth. The identification of the material parameters of aluminium films on different substrates results in a significant increase in yield stress and initial work–hardening rate for a reduction of the film thickness from 1.5 to 0.5 µm, while the elastic modulus and the extent of work hardening remain constant.