No abstract

Interrupted blanking experiments were performed to study the deformation behavior of AA6082 (T6) sheets. An intentional asymmetry was introduced by having the punch and the die of different edge radii. High-resolution microstructural montages of sheet cross-section were used to experimentally measure the shear strain field during each interrupted blanking experiment. This provided a comprehensive experimentally measured spatial–temporal shear strain field for the blanking process. The shear strain was found to be high and localized at the top and bottom surfaces of the sheet which was in contact with the punch and the die. The shear strain was observed to monotonically reduce and get diffused at the interior of the sheet. The strain distribution was also calculated by finite element simulations and found to be in good agreement with the experimentally measured strain distributions. However, the peak strains predicted by the finite element simulations were always marginally lower than those observed by the experimental observations.

In the present work, a critical analysis of the most-commonly used analytical models and recently introduced ANN-based models was performed to evaluate their predictive accuracy within and outside the experimental interval used to generate them. The high-temperature deformation behavior of a medium carbon steel was studied over a wide range of strains, strain rates, and temperatures using hot compression tests on a Gleeble-3800. The experimental flow curves were modeled using the Johnson–Cook, Modified-Zerilli–Armstrong, Hansel–Spittel, Arrhenius, and PTM models, as well as an ANN model. The mean absolute relative error and root-mean-squared error values were used to quantify the predictive accuracy of the models analyzed. The results indicated that the Johnson–Cook and Modified-Zerilli–Armstrong models had a significant error, while the Hansel–Spittel, PTM, and Arrhenius models were able to predict the behavior of this alloy. The ANN model showed excellent agreement between the predicted and experimental flow curves, with an error of less than 0.62%. To validate the performance, the ability to interpolate and extrapolate the experimental data was also tested. The Hansel–Spittel, PTM, and Arrhenius models showed good interpolation and extrapolation capabilities. However, the ANN model was the most-powerful of all the models.