A Full-Field Calibration Approach on Material Parameter Identification

In the recent years, the usage of HS-steels has risen significantly in the automotive field. Their characteristics, such as hardness and favorable weight to strength ratio, can increase safety, fuel efficiency and overall product profitability. In this context, for the design with this material it has become crucial to be able to characterize precisely HS-steels and accurately predict their failure in many complex conditions, to fully exploit their capabilities. One of the most accredited ways to approach the prediction of failure for a wide range of materials is the generalized incremental stress-state dependent damage model GISSMO. The model is highly flexible and provides a framework inside LS-DYNA in which failure parameters can be tuned to reproduce experimental data. The definition of the optimal parameters is an inverse problem, therefore it was implemented using LS-OPT. In this work, the experimental evaluation of the MS1500 was carried out using the digital image correlation (DIC). With such technology, the displacements’ field of the test specimen is recorded.The evalueted field was processed as a family of stress-strain curves (hyper-curves) and became the objective of the optimization. This approach is named full field calibration and in this work was split in two phases. First, the stress-strain curve of the material was defined, then the tuning of the GISSMO parameters was performed. To evaluate the effectiveness of the full field approach a parallel study was implemented. The same routine of optimization run with a single stress-strain curve, which was measured with an extensometer. The comparison between the results obtained with the traditional approach and the results obtained with the full field approach highlighted the strenghts and the limitations of the two methods.