TY - JOUR
T1 - Modified C/SiC composites with high ablation resistance
T2 - Data-driven intelligent design and experimental validation
AU - Du, Zirui
AU - Song, Chaokun
AU - Guan, Kang
AU - Rao, Pinggen
AU - Yang, Xiaohui
AU - Bai, Longteng
AU - Shi, Shengbo
AU - Wang, Jing
AU - Liu, Yongsheng
AU - Zeng, Qingfeng
N1 - Publisher Copyright:
© 2025 The American Ceramic Society.
PY - 2025/12
Y1 - 2025/12
N2 - 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.
AB - 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.
KW - C/SiC composite materials
KW - ZrB
KW - ablation resistance
KW - machine learning
UR - https://www.scopus.com/pages/publications/105014877138
U2 - 10.1111/jace.70209
DO - 10.1111/jace.70209
M3 - 文章
AN - SCOPUS:105014877138
SN - 0002-7820
VL - 108
JO - Journal of the American Ceramic Society
JF - Journal of the American Ceramic Society
IS - 12
M1 - e70209
ER -