This tutorial is part of the program of the 2nd MecaNano Workshop on Machine Learning for Micro- and Nano-Mechanics.
Understanding the behaviour of steel under applied stress is fundamental for predicting its performance in real applications. In this tutorial, we explore how to characterise different types of damage in dual-phase steel using panoramic electron microscopy images combined with advanced analysis techniques. The data and scripts used here are openly available, making it possible to reproduce and extend the work.
Repository with data and code: https://git.rwth-aachen.de/teaching_uk/matphysdesign/damageclassification
1. Introduction
When tensile stress is applied to steel, multiple microstructural damage mechanisms can be activated. Identifying and distinguishing these mechanisms is key for improving material design and predicting lifetime under load.
In this tutorial, we focus on the following damage types:
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Martensite Cracking – brittle cracks forming within martensite islands.
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Notch Effect – stress concentration at geometric irregularities.
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Interface Decohesion – debonding at ferrite–martensite interfaces.
Additionally, we account for two major challenges:
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Manufacturing-related artefacts – foreign particles embedded in the sample that mimic damage sites.
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Imaging artefacts – shadows and contrast distortions caused by the 3D geometry of the specimen under the electron beam.
Our starting point is a panoramic electron microscopy image of a dual-phase steel specimen, from which damage sites will be identified and classified.
2. Workflow Overview
The repository provides both data and code to reproduce the analysis. The general steps are:
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Acquire panoramic image of the steel specimen using a scanning electron microscope (SEM).
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Pre-process the image to remove artefacts (beam shadows, noise).
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Detect candidate damage sites (cracks, voids, notches).
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Classify damage mechanisms into martensite cracking, notch effect, or interface decohesion.
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Exclude false positives from inclusions or artefacts.
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Visualise and validate results on the large-area image.
The scripts in the repository automate parts of this workflow, and can be adapted for new datasets.
3. Related Work
This tutorial builds upon and connects to recent advances in large-area imaging and machine learning for microstructural damage analysis:
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Large-area SEM image analysis with deep learning
Kusche C, Reclik T, Freund M, Al-Samman T, Kerzel U, Korte-Kerzel S (2019)
Large-area, high-resolution characterisation and classification of damage mechanisms in dual-phase steel using deep learning.
PLoS ONE 14(5): e0216493. - 3D characterisation of damage in dual-phase steel
Setareh Medghalchi, Ehsan Karimi, Sang-Hyeok Lee, Benjamin Berkels, Ulrich Kerzel, Sandra Korte-Kerzel (2023)
Three-dimensional characterisation of deformation-induced damage in dual phase steel using deep learning.
Materials & Design, Volume 232, 112108.
https://doi.org/10.1016/j.matdes.2023.112108
4. Data and Code
All original data and scripts for reproducing this tutorial are openly available on GitLab: https://git.rwth-aachen.de/teaching_uk/matphysdesign/damageclassification