Advancing Material Models for Automotive Forming Simulations

Simulations in automotive industry need more advanced material models to achieve highly reliable forming and springback predictions. Conventional material models implemented in the FEM-simulation models are not capable to describe the plastic material behaviour during monotonic strain paths with sufficient accuracy. Recently, ESI and Corus co-operate on the implementation of an advanced material model in the FEM-code PAMSTAMP 2G. This applies to the strain hardening model, the influence of strain rate, and the description of the yield locus in these models. A subsequent challenge is the description of the material after a change of strain path.The use of advanced high strength steels in the automotive industry requires a description of plastic material behaviour of multiphase steels. The simplest variant is dual phase steel consisting of a ferritic and a martensitic phase. Multiphase materials also contain a bainitic phase in addition to the ferritic and martensitic phase. More physical descriptions of strain hardening than simple fitted Ludwik/Nadai curves are necessary.Methods to predict plastic behaviour of single-phase materials use a simple dislocation interaction model based on the formed cells structures only. At Corus, a new method is proposed to predict plastic behaviour of multiphase materials have to take hard phases into account, which deform less easily. The resulting deformation gradients create geometrically necessary dislocations. Additional micro-structural information such as morphology and size of hard phase particles or grains is necessary to derive the strain hardening models for this type of materials.Measurements available from the Numisheet benchmarks allow these models to be validated. At Corus, additional measured values are available from cross-die tests. This laboratory test can attain critical deformations by large variations in blank size and processing conditions. The tests are a powerful tool in optimising forming simulations prior to larger scale industrial validation