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Ductile damage at MnS inclusions in 16MnCrS5 steel - influence of the phase environment

  • Maximilian A. Wollenweber
  • , Tom Reclik
  • , Clara Reinhart
  • , Verena Maier-Kiener
  • , Talal Al-Samman
  • , Ulrich Kerzel
  • , Sandra Korte-Kerzel
  • RWTH Aachen

Research output: Contribution to journalArticleResearchpeer-review

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 languageEnglish
Article number150251
Number of pages10
JournalMaterials science and engineering: A, Structural materials: properties, microstructure and processing
Volume2026
Issue numberVolume 965, July
Early online date17 Apr 2026
DOIs
Publication statusPublished - Jul 2026

Bibliographical note

Publisher Copyright: © 2026 The Authors

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 9 - Industry, Innovation, and Infrastructure
    SDG 9 Industry, Innovation, and Infrastructure

Keywords

  • Machine learning
  • Microstructural damage
  • MnS
  • Scanning electron microscopy
  • Semantic segmentation
  • Spherical nanoindentation
  • Steel

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