Abstract
Forming-induced damage strongly affects the service life and mechanical properties of components made from 16MnCrS5 case-hardening steel. In this study, we quantitatively investigate how the local phase environment, specifically ferritic, pearlitic, and mixed regions, around damage sites associated with manganese sulphide (MnS) inclusions affects their nucleation and growth. Using a novel approach based on machine learning-driven segmentation of high-resolution scanning electron micrographs from in-situ tensile tests, we achieve reproducible and efficient identification of phases in ferritic–pearlitic microstructures and quantification of damage sites. Our results reveal that ferritic environments lead to increased damage prevalence when compared to mixed and pearlitic environments. In particular, the growth of damage sites is markedly facilitated when the local ferrite fraction is increased. We explain this with the low strain hardening capability of ferrite compared to pearlite, determined via spherical nanoindentation. In contrast, the nucleation probability is not strongly dependent on the immediate phase environment, as the yield strength of ferrite and pearlite are similar.
| Original language | English |
|---|---|
| Article number | 150251 |
| Number of pages | 10 |
| Journal | Materials science and engineering: A, Structural materials: properties, microstructure and processing |
| Volume | 2026 |
| Issue number | Volume 965, July |
| Early online date | 17 Apr 2026 |
| DOIs | |
| Publication status | Published - Jul 2026 |
Bibliographical note
Publisher Copyright: © 2026 The AuthorsUN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 9 Industry, Innovation, and Infrastructure
Keywords
- Machine learning
- Microstructural damage
- MnS
- Scanning electron microscopy
- Semantic segmentation
- Spherical nanoindentation
- Steel
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