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This study primarily aims to develop a robust modelling approach to capture complex material behavior of CP-Ti, appeared by high anisotropy, differential hardening due to anisotropy evolution, and flow behavior sensitive to strain rate and temperature, using artificial neural networks (ANNs). Plasticity is characterized by uniaxial tension and in-plane biaxial tension tests at temperatures of 0°C and 20°C with strain rates of 0.001 /s and 0.01 /s, and the results are used to calibrate the non-quadratic anisotropic Yld2000-3d yield function with respect to the plastic work. In order to predict the intricate plastic deformation with the temperature and strain rate effects, two distinct ANN models are developed; one is to capture the strain hardening behavior and the other to predict the anisotropic parameters in the chosen yield function. The developed ANN models predict an unseen dataset well, which is intermediate testing conditions at a temperature of 10°C and strain rate of 0.005 /s. The ANN models, being computationally stable and adhering to conventional constitutive equations, are implemented into a user material subroutine for the ductile fracture characterization of CP-Ti sheet using the hybrid experimental-numerical analysis. The favorable agreement between experimental data and numerical predictions, particularly using the ANN models with evolving anisotropic material parameters for the Yld2000-3d yield function, underscores the significance of differential hardening effect on the ductile fracture behavior and highlights the capabilities of ANN models to capture the complex plastic behavior of CP-Ti. The key parameters including stress triaxiality, Lode angle parameter, and equivalent plastic strain at the fracture location are extracted from the simulations, enabling the calibration of ductile fracture models, namely Johnson-Cook, Hosford-Coulomb, and Lou-2014, and construction of fracture envelopes. 

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. 

In this study, the ductile damage responses of high-strength 7000 series aluminum alloy (AA), AA 7075-T6 sheet samples, subjected to the plane strain deformation mode were investigated using finite element (FE) simulations. In the experiments, uniaxial tension (UT) and plane strain tension (PST) tests were conducted to characterize the plasticity and ductile damage behavior of the AA 7075-T6 sheet samples. The limiting dome height (LDH) and V-die air bending tests were conducted to evaluate the ductility of the material subjected to plastic deformation and friction between the tools, and the corresponding fractured samples were qualitatively analyzed in terms of dimples using fractography. FE simulations were performed to predict the ductility of the AA 7075-T6 sheet samples under plane strain deformation using an enhanced Gurson−Tvergaard−Needleman (GTN) model, namely the GTN-shear model. The model was improved by adding the shear dimple effect to the original GTN model. The predicted results in terms of the load–displacement curves and displacements at the onset of failure were in good agreement with experimental data from the aforementioned tests. Furthermore, virtual roll forming simulations were conducted using the GTN-shear model to determine the effect of the prediction on ductile behavior for industrial applications. 

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%. 

The automotive industry relies heavily on sheet metal forming processes for many components. Material data solely from uniaxial testing is insufficient to fully define the material behavior of the complex plastic deformation during numerical simulations of the forming processes. In-plane biaxial testing using a cruciform type specimen is a more comprehensive representation than the traditional uniaxial testing alone. Wide ranging biaxial stress states can be imposed by applying different loading conditions on each cruciform axis. However, this can create a challenge to achieve desired deformation paths due to the non-linear relationship between the control parameter, e.g., displacement, and the output of interest, e.g., strain path. In this paper, an interpolation method to develop the displacement control that produces a linear strain path with a desired strain ratio is revisited and expanded upon from the authors’ previous work [1,2]. In the first iteration, linear biaxial displacements were applied to the specimen and the corresponding strain paths were obtained from the numerical simulations. The non-linear strain paths, due to geometry effects of the specimen, were used to reverse engineer a new displacement path that results in a linear strain path. Interpolation is revisited to show increased success with a second iteration. Analysis of the simulation results shows that linear strain paths of a given model can be determined and improved by successive iterations of interpolating the strain data from adjacent deformation paths.