Material characterization based on instrumented and simulated indentation tests

This paper reviews various techniques to characterize material by interpreting load-displacement data from instrumented indentation tests. Scaling and dimensionless analysis was used to generalize the universal relationships between the characteristics of indentation curves and their material properties. The dimensionless functions were numerically calibrated via extensive finite element analysis. The interpretation of load-displacement curves from the established relationships was thus carried out by either solving higher order functions iteratively or employing neural networks. In this study, the advantages and disadvantages of these techniques are highlighted. Several issues in an instrumented indentation test such as friction, size effect and uniqueness of reverse analysis algorithms are discussed. In this study, a new reverse algorithm via neural network models to extract the mechanical properties by dual Berkovich and spherical indentation tests is introduced. The predicted material properties based on the proposed neural network models agree well with the numerical input data.