Determining the elastic modulus of film/substrate materials from instrumented indentation testing based on machine learning

Instrumented indentation testing has been widely used to determine the mechanical properties of materials. However, due to the substrate effect, the mechanical properties of each layer of film/substrate material becomes complicated to determine using this method. Through the finite element simulation, combined with a classic neural network method of multi-output multilayer perceptron (MLP), the relationship between the film/substrate material parameters (elastic modulus of film and substrate), normalized indentation depth (indentation depth/film thickness), and composite modulus is established. This established relationship is used to develop an indentation method to determine the elastic modulus of film/substrate. Prediction results through deep learning and the finite element simulation results are compared. The comparison results indicate that the predicted values of hard film/soft and soft film/hard substrate materials obtained from MLP are in good agreement with the simulation results. Indentation tests of Ni/304 and Cu/304 stainless steel were conducted to verify the trained neural network. The results indicate that elastic modulus of each layer predicted using the MLP is close to those obtained in the test. The results of this study can provide alternative research methods for evaluating the properties of film/substrate materials.