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Modified C/SiC composites with high ablation resistance: Data-driven intelligent design and experimental validation

  • Zirui Du
  • , Chaokun Song
  • , Kang Guan
  • , Pinggen Rao
  • , Xiaohui Yang
  • , Longteng Bai
  • , Shengbo Shi
  • , Jing Wang
  • , Yongsheng Liu
  • , Qingfeng Zeng
  • Guangdong Provincial Key Laboratory of Advanced Energy Storage Materials
  • South China University of Technology
  • Xi'an Aerospace Propulsion Institute
  • School of Astronautics
  • Northwestern Polytechnical University Xian
  • Tianmushan Laboratory

科研成果: 期刊稿件 › 文章 › 同行评审

4 引用 (Scopus)

摘要

Improving the ablation resistance of carbon fiber-reinforced silicon carbide (C/SiC) composites is essential to meet the stringent demands of ultra-high-temperature applications, but traditional empirical design approaches are resource-intensive and time-consuming. This study presents a novel data-driven methodology integrating machine learning (ML) with experimental validation to optimize C/SiC composite ablation resistance. Through systematic data preprocessing and feature analysis of 102 experimental samples, an XGBoost regression model was developed, achieving satisfactory prediction accuracy (mean absolute error < ∼0.075, mean squared error < ∼0.015, and coefficient of determination [R2] > ∼0.75) for ablation rate. SHapley Additive exPlanations (SHAP) analysis revealed that modifier properties, particularly standard enthalpy and melting point, predominantly influence ablation resistance. The ML-guided design strategy, implemented through Bayesian optimization, led to the successful fabrication of ZrB2-modified C/SiC composites with exceptional ablation resistance. The optimized composite, containing approximately 20.0 vol.% ZrB2, achieved a linear ablation rate of 2.083 µm/s under oxyacetylene torch testing, representing a significant improvement over conventional compositions. Microstructural analysis confirmed the formation of a dense SiO2‒B2O3 protective layer, validating the predicted mechanism of enhanced ablation resistance. This work establishes a robust framework for accelerated development of ultra-high-temperature ceramics and demonstrates the efficacy of ML-driven approaches in materials design optimization. An object-oriented software with interactive graphical user interface has been developed. These methodologies have been integrated into an interactive software, Modified C/SiC Ablation Rate intelligent design Software (MARS), creating an efficient tool for the accelerated design of C/SiC composites with tailored ablation performance.

源语言英语
期刊论文编号e70209
期刊Journal of the American Ceramic Society
卷108
期12
DOI
出版状态已出版 - 12月 2025

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