|About this Abstract
||2021 TMS Annual Meeting & Exhibition
||Deformation Induced Microstructural Modification
||Quantification and mechanism classification of deformation-induced damage using deep learning
||Sandra Korte-Kerzel, Setareh Medghalchi, Carl Kusche, Talal Al-Samman, Ulrich Kerzel
|On-Site Speaker (Planned)
High performance materials typically possess a complex structure at the microscale. Their properties exceed those of the individual components and their knowledge-based improvement therefore requires understanding beyond that of the components’ individual behaviour. Electron microscopy has been instrumental in unravelling the most important mechanisms of co-deformation and in-situ deformation experiments have emerged as a popular and accessible technique. However, a challenge remains: to achieve high spatial resolution and statistical relevance in combination. A high-throughput, deep-learning-based approach now gives us strain and microstructure dependent insights into the prevalent mechanisms for different strain paths. Aiming for the first time at automated classification of the majority of damage sites rather than only the most distinct, the new method also encourages us to expand current research past interpretation of exemplary cases of distinct damage sites towards the less clear-cut reality.
||Shaping and Forming, Characterization, Machine Learning