Berlin 2024 – scientific programme
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MM: Fachverband Metall- und Materialphysik
MM 20: Data Driven Material Science: Big Data and Workflows III
MM 20.10: Talk
Tuesday, March 19, 2024, 12:45–13:00, C 243
High-throughput damage quantification in steel and opportunities for damage-controlled forming processes — •Maximilian A. Wollenweber1, Jannik Gerlach2, Tom Reclik1, Clara Reinhart1, Sebastian Münstermann2, Talal Al-Samman1, and Sandra Korte-Kerzel1 — 1Institute for Physical Metallurgy and Materials Physics, RWTH Aachen University, Aachen, Germany — 2Institute of Metal Forming, RWTH Aachen University, Aachen, Germany
When trying to improve the mechanical properties of steel parts a frequently overlooked cause for degradation of mechanical properties is the prevalence of damage sites often present in the form of voids in the microstructure. Caused by the mechanical contrast of different phases leading to plastic inhomogeneities during the forming process, these damage sites majorly decrease the longevity and crash performance. Trying to reduce these damage sites to enable damage-controlled forming processes poses two big challenges: On the one hand damage sites need to be characterized in order to understand the microstructural effects on damage formation and growth. On the other hand, optimized strain paths and adjusted microstructures need to be conceptualized to minimize the effect of damage on the performance. In this work we want to exemplary show how to tackle both of these issues; how high-throughput scanning electron microscopy and artificial intelligence can be used to evaluate damage sites, as well as demonstrating how adjustments to the strain-path and different microstructural concepts harbor opportunities for damage-controlled forming processes.
Keywords: Steel; Microstructural damage; Machine learning; Scanning electron microscopy