The use of a simulative approach with representative volume elements (RVE’s) is particularly well suited to investigate the influence of different microstructural parameters on the damage behavior of a material. In order to statistically analyze the individual components of the microstructure (e.g. geometric structure of grains and inclusions), well-known distribution functions such as logNormal/Gamma are normally used, but these do not take into account the interdependencies between the different parameters. However, newer approaches like machine learning techniques can only describe one phase of a single material at a time. Therefore, in this study, we extended an existing Wasserstein Generative Adversarial Network (WGAN) to a Conditional Wasserstein GAN with Gradient Penalty (CWGAN-GP), with which it is possible to process multiple materials/phases simultaneously. Training this algorithm on different steels and associated inclusions showed that a single trained network can generate synthetic microstructure for all different phases and materials with very high quality. A newly implemented evaluation method using the regularized Wasserstein-distance confirmed the excellent agreement of the real data with the synthetic data for all phases/materials. As a use case for our algorithm, the influence of different inclusions on the stress accumulation and concentration of X65 pipeline steel was investigated to find initiation sites for damage in the material. These investigations showed a pronounced correlation between stress concentration and inclusion parameters, thus confirming the usefulness of the CWGAN-GP as an input-generator for RVE’s.

The crushing of crash boxes is a complex phenomenon characterized by a strong interaction of structural and material properties. Many attempts to improve their energy absorption by adopting one aspect resulted in unacceptable unprogressive bucking. Thus, special considerations are required for crash box optimization including both material and structural effects. In this study, the structural behavior of the crash box is analyzed and a new design approach is introduced to fulfill these requirements. Currently, components, processes, and materials are mainly developed independently. However, to exploit the full potential of modern materials in component design, integrative development work is necessary. Component performance-based requirements and corresponding local material properties must be taken into account concurrently. In this work, a component-driven material design approach is presented, in which local-material-property requirements are derived from component simulations. This new approach is demonstrated by the use case of a quasistatically deformed crash box produced out of steel DP600 for the optimization target “energy absorption”. The finite element simulations were carried out on the crash box, and required material properties for improving the crash box performance were derived based on the simulations. Heat treatment strategies were developed afterwards and experimentally validated to fulfill these requirements. The finite element (FE) simulations based on the experimentally extracted material properties reveal the potential of the component-driven material design approach to improve the crash box performance. The introduced approach enables exploiting the full energy-absorption capacity of the material while ensuring the desired service behavior of the component.