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Coupled damage evolution of Cf/SiC composites across a full oxygen concentration gradient at elevated temperature via synchrotron in-situ CT and deep learning

  • Beijing Institute of Technology

Research output: Contribution to journal › Article › peer-review

Abstract

To elucidate the effect of environmental oxygen concentration on damage evolution in Cf/SiC composites at high temperature, an integrated platform incorporating ultrahigh‑temperature in‑situ loading with synchrotron‑radiation μCT (SR‑μCT) was constructed. This platform enabled real‑time observation of coupled thermo‑mechanical‑oxidative damage in Cf/SiC at 1400 °C under 0, 0.5 vol.%, 5 vol.%, and 21 vol.% O2. Combined with the nnU‑Net deep‑learning framework, the 3D evolution of coating cracks, voids, oxidation damage, and delamination was visualised and quantified. Integrating SR‑μCT with fractography elucidated the sequential evolution from damage initiation to final failure. Tensile strength decreased with increasing oxygen concentration. Under inert conditions, coating cracks propagated periodically with continuous nucleation, void morphology remained stable, and failure was abrupt and brittle. At O2 ≥ 0.5 vol.%, oxidation‑induced damage initially propagated along pre‑existing coating cracks; fibres in the crack‑contact zone were oxidised first, and damage extended along the fibre orientation, resulting in a 3D damage field aligned with the crack path. At O2 ≥ 5 vol.%, oxidation induced new microvoids whose growth and coalescence increased void volume and formed a 3D network. Collectively, these processes established a cycle where voids accelerated oxygen diffusion, accelerated diffusion induced further damage, and damage produced more voids. This work provides an effective four-dimensional analysis method for investigating the coupled evolution of multiple damage modes in ceramic-matrix composites under high-temperature oxidation.

Original languageEnglish
Pages (from-to)169-181
Number of pages13
JournalJournal of Materials Science and Technology
Volume274
DOIs
Publication statusPublished - 10 Dec 2026

Keywords

  • C/SiC composite materials
  • Deep learning
  • Oxidation damage
  • Oxygen concentration
  • Synchrotron X-ray micro-CT

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