Parameters identification and validation of plastic-damage model of 304 stainless steel by small punch test at ambient temperature

Abstract

Small punch test is an advantageous alternative to standard tests which need a substantial amount of material and are quite time-consuming. Finite element simulation along with an optimization algorithm can be employed in order to identify the elastic, plastic and damage parameters of a material from force-displacement curve. In this research, the plastic properties and Gurson-Tvergaard-Needleman damage model parameters have been identified for 304 stainless steel at ambient temperature using force–displacement curve obtained from small punch test. For identification of parameters, an optimization procedure by involving Genetic Algorithm and Neural Networks has been proposed. To validate the identified parameters, tensile test on notched specimen has been performed. The experimental force–displacement curve and the simulation one, obtained by implementing the identified properties in ABAQUS, have been compared. These two curves are rather similar in most regions, however, there are some differences especially in the final stage. Differences in the nature of small punch test and tension test, and also differences in the stress triaxiality and friction coefficient effects, are the main reasons the two curves do not completely match.