Damage mechanics based approach in failure predication of draw forming procecesses

Abstract

In a cup draw forming operation, the desired shape results from the material hardening process under controlled plastic deformation and the springback phenomena. In this study, a mechanics-of-deformation approach is developed based on damage variables and large plastic deformation. The approach is then employed to estimate the onset of the material damage event and the location of fracture based on the mechanics response of the metal blank. Draw forming behavior of low carbon steel is examined as a case study. The loading rate is conducted at a slow loading response of the steels in the large deformation of the draw forming processes. Axisymmetric and 3D solid models are developed for finite element (FE) simulations to gain insight into the evolution of internal states and damage in the steel blanks during the draw forming process. In the FE simulation, Johnson-Cook constitutive model with isotropic hardening rule is employed. The Rice-Tracey ductile damage criterion is employed to indicate damage initiation event along with a linear energy-displacement relation for damage evolution rule. Results show that while the applied loading (tool displacement) is quasi-static corresponding to the strain rate of 0.001 sec-1, the maximum plastic strain rate at fracture could reach 100 times greater at the critical material flow region. Failure of the deforming steel blank is localized with excessive plastic deformation. While the onset of damage can be efficiently predicted using the axisymmetric FE model with damage-based model, the subsequent damage evolution of the localized ductile failure requires a 3D continuum FE model. The predicted tool load-displacement response is employed in validating the FE model. Effects of drawing parameters including drawing speed, blank holder force and die clearance on the resulting deformation of the drawn cup-shape part are established. Based on the response of the mechanics-of-deformation, the established failure prediction approach is proven more accurate and reliable.