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This study presents a novel machine learning approach for predicting the anisotropic parameters of the Yld20002d non-quadratic yield function using a hole expansion test. Heterogeneous stress-strain fields during the test substitute for multiple experiments required in the conventional parameter identification approach. An artificial neural network model for the parameter prediction is developed using a virtually generated training dataset composed of strains from hole expansion simulations, performed using randomly selected anisotropic parameters. The developed model predicts the Yld20002d parameters for AA6022-T4 based on the measured strain field from a hole expansion experiment, and the parameter results are evaluated by comparing anisotropy in uniaxial tension tests, the yield locus, and thinning variation in hole expansion test. 

A multi-interpolation method is proposed to determine the displacement trajectory along each axis of a cruciform specimen with the goal of achieving a linear stress path, corresponding to a constant stress triaxiality, in the center of the custom-designed, non-standard specimen during in-plane biaxial testing. Finite element simulations are used to obtain the stress path from the given displacement trajectory, which is the displacement histories imposed on the specimen loading arms. In every iteration, the displacement trajectory is updated using the interpolation between the target stress path and adjacent ones on each side of the curve. The iterations are repeated until a linearity tolerance is satisfied. In this study, the material is an austenitic stainless steel, SS316L, with the Hockett–Sherby isotropic hardening model and Yld2004-18p non-quadratic anisotropic yield function. The method is demonstrated for five stress states between pure shear and equibiaxial tension. The results show the successful determination of a displacement trajectory for the non-standard cruciform specimen so that a linear stress path and constant triaxiality at the area of interest are achieved.