Plastic properties determination using virtual dynamic spherical indentation test and machine learning algorithms

Nondestructive experiments are widely employed for determining mechanical properties. In this work, a spherical impact test is virtually performed to determine the plastic properties of a thick aluminum plate. The finite element model is validated experimentally, and several simulations are then performed in accordance with the design of the experiment program. Several algorithms, including support vector machine, Gaussian process regression (GPR), and nonlinear regressions (second- and third-order polynomials) as machine learning techniques, are employed to estimate the material plastic properties. The indentation depth and indentation radius are considered as input variables to predict the tangent modulus (TM) and yield stress (YS). Results reveal that the second-order polynomial and GPR methods realize better performance in terms of determination coefficient and root mean square error criteria in assessing YS.