Numerical Prediction of Forming Car Body Parts with Emphasis on Springback

Abstract

Numerical simulation is an important tool which can be used for designing parts and production processes. Springback prediction, with the use of numerical simulation, is essential for the reduction of tool try-outs through the design of the forming tools with die compensation, therefore, increasing the dimensional accuracy of stamped parts and reducing manufacturing costs. In this work, numerical simulation was used for performing the springback analysis of car body stamping made of aluminium alloy AA6451-T4. The finite element analysis (FEM) based software PAM-STAMP 2G was used for performing the forming and springback simulations. These predictions were conducted with various combinations of material models to achieve accurate springback prediction results. Six types of yield functions (Barlat89, Barlat2000, Vegter-Lite, Hill90, Hill48 isotropic, and Hill48 orthotropic) were used in combination with the Voce hardening model. Springback analysis was conducted in three sections of the formed part; the numerical results were compared with the experimental values. It was found that the combinations of Barlat's yield functions and the Voce hardening law were most accurate in terms of springback prediction. Additionally, it was found that the phenomena that were investigated, which are required for the determination of the kinematic hardening model, such as the change of Young's modulus E, the transient behaviour, work-hardening stagnation, and permanent softening, were not observed in the aluminium alloy studied.

Effects of Hardening Model and Variation of Elastic Modulus on Springback Prediction in Roll Forming

“…Sheet metal forming processes like roll forming, bending, stretching, deep drawing [1] and stamping are widely used in the production processes, especially in the automobile industries [2]. The management of sheet properties can also be successfully implemented by creating a special internal architecture, for example, by differential heat treatment [3].…”

Physical Modelling and Numerical Simulation of the Deep Drawing Process of a Box-Shaped Product Focused on Material Limits Determination

“…The results comply with [24] and were also used for mild steel and drawing quality steel when the deep drawing process was numerically simulated [16,17,21,25]. The selection of a proper mathematical model for the yield locus and hardening law is even more important for metal sheets and processes, especially when new types of steel or springback phenomena are simulated [17,25,26]. To define these models, more tests than just measuring uniaxial tension with a tensile test or biaxial tension with the Bulge test are necessary [15,32].…”

“…The springback simulations were conducted with six yield functions (Barlat89, Barlat2000, Vegter-Lite, Hill90, Hill48 isotropic, and Hill48 orthotropic) combined with the Voce hardening model. Springback analysis was done in three sections, and the results were compared with the experimental values [26].…”

Springback Prediction in Sheet Metal Forming, Based on Finite Element Analysis and Artificial Neural Network Approach

“…Many approaches concerning sheet metal forming and springback prediction were conducted by different researchers. For example, Mulidran et al (2018) [5] studied the springback effect on a car body stamped part, made of AA6451-T4, by using PAM-STAMP 2G. Various material models combinations were implemented in order to achieve accuracy in the springback prediction results.…”