September 5, 2025Journal of the American Ceramic Society

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

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Ablation rate of modified C/SiC composites decreased to 2.083 µm/s, showing superior resistance.
Machine learning model achieved a coefficient of determination (R²) > 0.75 for predicting ablation performance.
Bayesian optimization led to successful fabrication of ZrB2-enhanced C/SiC composites with improved durability.
Microstructural analysis confirmed the formation of protective SiO2–B2O3 layer, validating design predictions.

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Implication

This study demonstrates machine learning optimization for C/SiC composite ablation resistance, indicating significant improvements through innovative methodologies.

Cite This Study

Du et al. (2025) studied this question.

synapsesocial.com/papers/68c23a2cb210217d6477f312 https://doi.org/10.1111/jace.70209

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Implication

This study demonstrates machine learning optimization for C/SiC composite ablation resistance, indicating significant improvements through innovative methodologies.

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

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Implication

Cite This Study

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

Key Points

Discussion

Implication

Cite This Study

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