Predicting failure progressions of structural materials via deep learning based on void topology

Leslie Ching Ow Tiong, Gunjick Lee, Gyeong Hoon Yi, Seok Su Sohn, Donghun Kim

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Despite considerable mechanics modeling-based efforts, accurate predictions of failure progressions of structural materials remain challenging in real-world environments primarily due to complex damage factors and defect evolutions. Here, we report a novel deep learning-based method for predicting failure properties based on defect state evolutions, which enables the full reflection of the damage accumulated in a material until the time of its examination. The method uniquely combines nondestructive X-ray computed tomography (X-CT), persistent homology (PH), and deep learning. It exploits the PH-encoded features from 3D X-CT images as its only input, and outputs failure-related properties. Using two fracture datasets based on low-alloy ferritic steel as a representative structural material, the method was demonstrated to reliably classify or predict the local strain (tensile dataset) and fracture progress (fatigue dataset). The excellent deep learning performances are attributed to both PH analysis and multimodal learning, where key topological features of internal voids, such as their size, density, and distributions, are precisely quantified. The proposed method enables accurate prediction of failure-related properties at the time of material examination based on void topology progressions, and can be extended to various nondestructive failure tests for practical use.

Original languageEnglish
Article number118862
JournalActa Materialia
Volume250
DOIs
Publication statusPublished - 2023 May 15

Bibliographical note

Funding Information:
This work was supported by Samsung Research Funding & Incubation Center of Samsung Electronics under Project Number SRFC-MA1902–04. We would like to give a special thanks to Dr. Jeong Min Park from Korea Institute of Materials Science and Mr. Yeon Taek Choi from Pohang University of Science and Technology for DIC measurements.

Funding Information:
This work was supported by Samsung Research Funding & Incubation Center of Samsung Electronics under Project Number SRFC-MA1902–04. We would like to give a special thanks to Dr. Jeong Min Park from Korea Institute of Materials Science and Mr. Yeon Taek Choi from Pohang University of Science and Technology for DIC measurements.

Publisher Copyright:
© 2023 Acta Materialia Inc.

Keywords

  • Deep learning
  • Failure prediction
  • Persistent homology
  • Structural material
  • X-ray computed tomography (X-CT)

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Ceramics and Composites
  • Polymers and Plastics
  • Metals and Alloys

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