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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. 

Double-sided incremental forming (DSIF) is a die-less sheet metal forming process capable of fabricating complex parts. The flexibility of DSIF can be used for in-situ mechanical properties alteration, e.g., by controlling deformation-induced martensite transformation of austenitic stainless steels. In this paper, SS304L is deformed using DSIF at three different cooling conditions and two different tool paths to affect the martensite transformation. Additionally, finite element analyses were used to understandthe effect of tool paths on springback and plastic strain. Implementing a reforming tool path at the lowest achievable temperature resulted in a martensite volume fraction as high as 95%. 

In this paper, finite element analyses were conducted to investigate the stress and strain states resulting from varying the deformation of stainless steel 316L under biaxial loading. To that end, a biaxial specimen geometry was designed in collaboration with the US National Institute of Standards and Technology (NIST) to achieve large and uniform strain values in the central pocket region. Special care was taken to ensure that the specimen design could be readily manufactured with available resources. Simultaneously, the specimen design criteria required an acceptable strain uniformity in a sufficiently large pocket section to allow for accurate deformation and austenite to martensite phase fraction measurements. This demonstrates the concept of altering the final material properties through stress superposition. Numerical results show that nearly linear curves were observed in the strain path plots. The minimum uniform deformation area for the 4:1 case had a radius of ∼1 mm, which is sufficient for experimental analyses, e.g., digital imaging correlation and electron beam backscatter diffraction. As an application for such heterogeneous materials, patient specific trauma fixation hardware, which are surgically implanted to set broken bones during healing, require high strength in areas where screws are located, i.e., martensite phase, yet low weight elsewhere.