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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

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 [R 2 ] > ∼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 ZrB 2 -modified C/SiC composites with exceptional ablation resistance. The optimized composite, containing approximately 20.0 vol.% ZrB 2 , 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 SiO 2 ‒B 2 O 3 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.