Integrated hardening-anisotropy identification for stainless steel 18/10 using Barlat yield criterion and Lankford coefficients

Accurate numerical simulation of sheet metal forming processes requires robust constitutive models capable of capturing the anisotropic elastoplastic behavior of metallic materials under complex loading paths. For thin metallic sheets subjected to large strains, constitutive laws must accurately incorporate plastic anisotropy, non-linear hardening, and loading direction sensitivity to minimize industrial lead times and optimize process parameters. This work addresses these challenges by proposing a robust, single-loop optimization framework for modeling and identifying the anisotropic elastoplastic parameters of 18/10 stainless steel thin sheets. Unlike traditional decoupled sequential calibration routines that introduce internal mathematical inconsistencies, the proposed multi-level identification strategy simultaneously integrates directional off-axis stress–strain curves and experimental Lankford coefficients ( r -values) derived from mechanical extensometry into a unified calibration loop. The single-loop optimization framework demonstrates exceptional predictive accuracy, restricting the average root-mean-square error for simultaneous flow stress and r -value predictions to under 3% across all evaluated orientations. Furthermore, treating the non-quadratic Barlat Yld2000-2d shape factor as a free calibration variable allows the algorithm to converge to an optimal non-integer value of m  = 6.1257, providing the exact geometric flexibility required to capture the real experimental planar anisotropy and sharp yield topography of this austenitic grade. The validated framework provides a physically consistent, computationally efficient virtual prototyping tool that significantly reduces reliance on empirical trial-and-error adjustments in finite element codes for digital manufacturing applications.