This paper describes a 3D finite element model used for optimizing the deep-drawing formability of a commercially pure titanium cap designed for the cosmetic industry. The results obtained from a specific tooling and various tensile tests highlight a strong anisotropic behavior of the material (earing profile on the parts and Lankford coefficients very sensitive to the loading direction). The mechanical behavior taking this anisotropy into account is described according to an elastic-plastic model based on the quadratic Hill's criterion. A special attention is paid to studying the sensitivity of the FEM predictions with respect to the numerical parameters. The type and number of elements in the thickness of the blank and the friction coefficient have a significant influence on the numerical results. The comparison with the experiments taking the springback into account shows that the FE model is suitable for describing the behavior of titanium during a forming process such as deep-drawing. A Forming Limit Diagram is finally given to predict the feasibility and optimize the forming operation.A. Le Port · F. Toussaint (B) · R. Arrieux Laboratoire Systèmes et Matériaux pour la Mécatronique
Abstract. Surface defects can develop on automotive exterior panels after drawing and flanging steps, during springback and may alter significantly the vehicle quality. These defects are characterized by a depth below 0.5 mm and are then difficult to detect or predict numerically. This study focuses on a L-shaped part designed on purpose to reproduce at a small scale surface defects that occur after flanging. Dimensions of these defects are measured from profiles obtained with a tridimensional measuring machine. The investigation of the influence of the flanging height and flanging speed shows than neither of these parameters have impact on the surface defect . The numerical simulation of the flanging process predict the surface defect but with a lower depth than the experimental defect.
Several surface defects can develop on automotive exterior panels during the forming operations. They alter the vehicle aesthetic and their severity increases with the decrease of sheet thickness. These defects are difficult to characterize experimentally and reproduce numerically because of their depth below 0.5 mm and the complexity of the parts on which they develop. This study proposes a method that uses curvature of profiles to detect and characterize surface defects. The method has applied to a laboratory case to study the influence of tool parameters. Numerical simulations have been carried out and lead to a minimization of the severity of surface defects, but they allow to reproduce their shape.
Surface defects are small concave imperfections that can develop during forming on outer convex panels of automotive parts like doors. They occur during springback steps, after drawing in the vicinity of bending over a curved line and flanging/hemming in the vicinity of the upper corner of a door. They can alter significantly the final quality of the automobile and it is of primary importance to deal with them as early as possible in the design of the forming tools. The aim of this work is to reproduce at the laboratory scale such a defect, in the case of the flanging along a curved edge, made of two orthogonal straight part of length 50 mm and joint by a curved line. A dedicated device has been designed and steel samples were tested. Each sample was measured initially (after laser cutting) and after flanging, with a 3D measuring machine. 2D profiles were extracted and the curvature was calculated. Surface defects were defined between points where the curvature sign changed. Isovalues of surface defect depth could then be plotted, thus displaying also the spatial geometry on the part surface. An experimental database has been created on the influence of process parameters like the flanging height and the flanging radius. Numerical simulations have been performed with the finite element code Abaqus to predict the occurrence of such surface defects and to analyze stress and strain distribution within the defect area.
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