Steel is one of our most versatile material and we build everything from it from cars to skyscrapers. To improve the material further and develop the “next generation steel”, we need to understand its properties on a microscopic scale.

In collaboration with the Institute for Physical Metallurgy and Materials Physics at RWTH Aachen University we investigate the behaviour of steel under stress using artificial intelligence. One of the key challenges in this area of research is to investigate statistical effects: Human experts can only look at a few damage sites manually - but if we want to understand the material on a larger scale, we need to look at a large number of damage sites and understand their properties.

Here are the most recent publications:

  • K. Rejiba, S. Lee, Ch. Gasper, M. Freund, S. Korte-Kerzel, U. Kerzel (2025) Towards Defect Phase Diagrams: From Research Data Management to Automated Workflows, arXiv: 2511.01942
  • T. Reclik, S. Medghalchi, P. Schumacher, M. A. Wollenweber, T. Al-Samman, S. Korte-Kerzel, U. Kerzel (2025) Resolution enhancement of scanning electron micrographs using artificial intelligence, Materials & Design, Vol 253 (113955), DOI: 10.1016/j.matdes.2025.113955, arXiv: 2410.03746
  • Z. Xie, A. Atila, J. Guénolé, S. Korte-Kerzel, U. Kerzel (2025) Predicting Grain Boundary Segregation in Magnesium Alloys: An Atomistically Informed Machine Learning Approach, Journal of Magnesium and Alloys, DOI: 10.1016/j.jma.2025.03.021, arXiv: 2407.14148
  • S. Medghalchi, J. Kortmann, S. Lee, E. Karimi, U. Kerzel, S. Korte-Kerzel (2024) Automated Segmentation of Large Image Datasets using Artificial Intelligence for Microstructure Characterisation, Damage Analysis and High-Throughput Modelling Input, Materials & Design, 113031, DOI: 10.1016/j.matdes.2024.113031, arXiv: 2401.01147
  • S. Medghalchi, E. Karimi, S. Lee, B. Berkels, U. Kerzel, S. Korte-Kerzel (2023) Three-Dimensional Damage Characterisation in Dual Phase Steel using Deep Learning, Materials & Design, Vol 232, 112108, DOI: j.matdes.2023.112108, arXiv: 2303.05869
  • M. Wollenweber, S. Medghalchi, L. Guimarães, N. Lohrey, C. Kusche, U. Kerzel, T. Al-Samman, S. Korte-Kerzel (2023) On the damage behaviour in dual-phase DP800 steel deformed in single and combined strain paths, Materials and Design Vol 231, 112015, DOI: 10.1016/j.matdes.2023.112016
  • S. Medghalchi, M. Zubair, E. Karimi, S. Sandlöbes-Haut, U. Kerzel, S. Korte-Kerzel (2023) Determination of the Rate Dependence of Damage Formation in Metallic-Intermetallic Mg–Al–Ca Composites at Elevated Temperature using Panoramic Image Analysis, Advanced Engineering Materials, DOI: 10.1002/adem.202300956, arXiv: 2303.10477
  • S. Medghalchi, C. Kusche, E. Karimi, U. Kerzel, S. Korte-Kerzel (2020) Damage Analysis in Dual-Phase Steel Using Deep Learning: Transfer from Uniaxial to Biaxial Straining Conditions by Image Data Augmentation, JOM (2020). DOI: 10.1007/s11837-020-04404-0
  • C. Kusche, T. Reclik, M. Freund, T. Al-Samman, U. Kerzel, S. Korte-Kerzel (2019) High-resolution, yet statistically relevant, analysis of damage in DP steel using artificial intelligence, PLoS ONE 14(5): e0216493, DOI: 10.1371/journal.pone.0216493, arXiv: cond-mat.mtrl-sci/1809.09657