In this paper, the Self-Optimizing Inverse Method (Self-OPTIM) has been experimentally verified by identifying constitutive parameters solely based on prescribed boundary loadings without full-field displacements. Recently the Self-OPTIM methodology was developed as a computational inverse analysis tool that can identify parameters of nonlinear material constitutive models. However, the methodology was demonstrated only by numerically simulated testing with full-field displacement fields and prescribed boundary loadings. The Self-OPTIM is capable of identifying parameters of the chosen class of material constitutive models through minimization of an implicit objective function defined as a function of full-field stress and strain fields in the optimization process. The unique advantages of the Self-OPTIM includes: 1) model independency that is expected to open up a wide range of applications for various engineering simulations; 2) capabilities of parameter identification based solely on global measurements of boundary forces and displacements. In this paper, the Self-OPTIM inverse method is experimentally verified by using two different shapes of specimens made of AISI 1095 steel: 1) dog-bone and 2) notched specimens under a loading and unloading course. Parameters of a cyclic plasticity model with nonlinear kinematic hardening rule and associated flow theory are identified by the Self-OPTIM. Multiple tests and the inverse simulations are conducted to ensure consistent performance of the Self-OPTIM. The identified parameters are successively used to reconstruct the material response.
