An Alternative Approach to Determine Material Characteristics Using Spherical Indentation and Neural Networks for Bulk Metals

Abstract Material characteristics such as Young modulus, yield, and ultimate stresses are often considered as fundamental material parameters. Determination of material characteristics using the instrumented indentation test has gained interest among many researchers. The output of a spherical indentation test is usually the load-penetration (P-h) curve which is used to determine the Hollomon’s equation coefficients. Ideally, the elastic deformation of the sphere is to be excluded from the total displacement. However, the available techniques to omit the elastic deformation of the sphere are difficult-to-use and time consuming. In the present work, a noticeably simplified method is proposed to determine the load-displacement curve, preserving the required accuracy. The coefficients of Hollomon’s equation are then determined using the spherical indentation. The proposed method has also the ability to specify the unloading curve at each point of interest, even if the experimental data of the unloading procedure at that point is not available. Finally, by training a neural network and extracting the weights of its layers, an equation governing the network is presented explicitly. This expression makes the neural network easy to use. Furthermore, the proposed method is verified using the experimental results and method and experiment are shown to be in good agreement.