Novel approach for sheet metal constitutive parameters identification based on shape index and multiple regression

Abstract

To accurately predict the behavior and mechanical damage of sheet metal during hot forming process, the use of advanced thermomechanical modeling and precise identification of the constitutive parameter are required. This paper proposes a novel identification approach for constitutive parameters that relies on multiple regression and the shape index of experimental data curves. This approach is used to determine the material constants associated with the Johnson-Cook material and failure models for steel and aluminum sheets. Therefore, the obtained results approve the applicability and high accuracy of the identification procedure for the damage and the constitutive parameters of sheet metal, which allows for substantial time savings. Furthermore, to evaluate the formability of sheet metal, experimental forming tests at various temperatures were conducted. The identification process enhances the prediction of forming results, as shown by the fact that the numerical simulation of Erichsen and square die hydroforming tests using identified Johnson-Cook parameters agreed with the results of these tests carried out at different forming conditions. Moreover, the thickness measurement shows the ability of the developed model to properly predict the thickness of deformed shapes.