Tutorials

Posts related to each release of a Tutorial Video

Explainable Machine Learning – MecaNano Tutorial Series

This tutorial focuses on explainable machine learning for materials mechanics and nanomechanical testing. The session discusses why explainability is essential when machine-learning models are used to analyse experimental datasets, where the results must remain physically meaningful and scientifically defensible. It introduces strategies to understand model decisions, identify relevant features, evaluate model reliability, and avoid black-box conclusions that cannot be connected to the underlying material behaviour or experimental conditions.

Tutorial Slides

The following documents can be read directly from this page but are protected from downloading.

1. Overview of Gaussian Processes

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Machine learning bases and advanced applications for nanoindentation data analysis – MecaNano Tutorial Series

This tutorial introduces the foundations of machine learning for nanoindentation and nanomechanical data analysis. The session covers the basic concepts of supervised and unsupervised learning, feature extraction from indentation curves, clustering and classification of indentation datasets, analysis of high-throughput nanoindentation maps, and advanced workflows based on the full load-displacement curve. It discusses how data-driven methods can support phase identification, detection of anomalous curves, interpretation of mechanical populations, and integration with correlative microstructural information.

Tutorial Slides

The following documents can be read directly from this page but are protected from downloading.

Slides Deck

CNNs for damage assessment in steels – MecaNano Tutorial

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:

  • Martensite Cracking – brittle cracks forming within martensite islands.

  • Notch Effect – stress concentration at geometric irregularities.

  • Interface Decohesion – debonding at ferrite–martensite interfaces.

Additionally, we account for two major challenges:

  • Manufacturing-related artefacts – foreign particles embedded in the sample that mimic damage sites.

  • 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:

  1. Acquire panoramic image of the steel specimen using a scanning electron microscope (SEM).

  2. Pre-process the image to remove artefacts (beam shadows, noise).

  3. Detect candidate damage sites (cracks, voids, notches).

  4. Classify damage mechanisms into martensite cracking, notch effect, or interface decohesion.

  5. Exclude false positives from inclusions or artefacts.

  6. 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:

  • 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

Gaussian Process Regression — MecaNano Tutorial Series

This tutorial introduces Gaussian Process Regression (GPR), a non-parametric probabilistic approach widely adopted for interpolation, regression, and uncertainty quantification in materials science. The session explores both 1D and 2D inference, implemented with pyro.contrib.gp and PyTorch, with examples crafted for clarity and practical use.

Tutorial Slides

The following documents can be read directly from this page but are protected from downloading.

1. Overview of Gaussian Processes

2. Univariate GP Regression

3. Bivariate GP Regression

Note: Download buttons have been disabled for the embedded documents to protect the tutorial content. Please contact the authors for access.