A proper knowledge of the mechanical properties and residual stresses of materials has a significant role in the prediction of engineering failures. Indentation is a simple, nondestructive test that is capable of estimating both residual stresses and mechanical properties. In frequent studies, the response of materials during the indentation process has been used as a key parameter to distinguish different substances. Here, a state-of-the-art method with no undesirable restriction is suggested to attain the work hardening exponent, yield strength, and planar nonequibiaxial residual stresses. In the current work, an extensive series of Knoop indentation simulations were performed using two indenter angles. Subsequently, a precise observation was made in order to find existent relationships. A local method was employed based on characteristics of similar materials to obtain stress-free sample parameters through a genetic algorithm, and then another error function was defined in order to measure the yield strength and work hardening exponent. After the determination of the mechanical properties and the stress-free sample’s parameters, a particular and precise categorization was made. Then, neural network analysis was employed to derive planar residual stresses. Experimental validation was conducted using six types of aluminum and steel specimens. The results confirmed a good agreement between the test data and those predicted using the suggested procedure.
