Multi-Objective Inverse Identification of Swift Flow Stress Parameters and Coulomb Friction Coefficient for AA6061 Aluminum Alloy Using Compression Testing

Abstract In this study, a multi-objective inverse identification framework is developed to simultaneously determine the material flow stress parameters and the friction condition for AA6061–T6 under cold compression. The proposed approach combines a single compression experiment with systematic finite element simulations organized through a Box–Behnken design, surrogate modeling of numerical responses, and Pareto-based multi-objective optimization using the NSGA-II algorithm. The inverse identification is based on force–time and geometric responses extracted from the compression test and corresponding simulations. Response surface models are constructed and subsequently employed as surrogate models in the multi-objective optimization procedure, enabling robust exploration of the admissible parameter space. The identified results demonstrate strong and stable convergence of the Swift hardening parameters for AA6061–T6. The strength coefficient K is identified within a narrow range of approximately 358.52–361.13 MPa, while the strain-hardening exponent n converges to a value close to 0.088. The Coulomb friction coefficient m consistently converges toward the lower bound of the investigated domain, reaching a unique value of approximately 0.05. This value is physically reasonable for lubricated cold compression of aluminum alloys and is interpreted as a representative friction level constrained by the experimental observability of the present compression-based inverse framework. The results confirm that reliable and physically consistent identification of both material hardening behavior and friction conditions can be achieved from a single compression test by combining finite element simulations, design of experiments, and Pareto-based multi-objective optimization.