Machine Learning Approaches for Predicting the Ablation Performance of Ceramic Matrix Composites

• Published in: Journal of composites science Vol. 8; no. 3; p. 96
• Main Authors: Deb, Jayanta Bhusan, Gou, Jihua, Song, Haonan, Maiti, Chiranjit
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.03.2024
• Subjects: Ablation; Aircraft engines; Algorithms; Analysis; Boron nitride; Carbon fibers; Ceramic matrix composites; Ceramic tools; Ceramics; Composite materials; Continuous fibers; Corrosion resistance; Decision trees; Electrical resistivity; Errors; extreme gradient boosting; Fracture toughness; Gas turbine engines; Gas turbines; gradient boosting; Machine learning; Mechanical properties; Nanocomposites; Nanoparticles; Nuclear facilities; Nuclear reactors; Oxidation; Oxidation resistance; oxyacetylene torch test; Performance evaluation; Performance prediction; Polymers; Reentry vehicles; Regression models; Silicon carbide; Support vector machines; Temperature; Tensile strength; Thermal conductivity; Thermal cycling
• ISSN: 2504-477X; 2504-477X
• DOI: 10.3390/jcs8030096

Materials used in aircraft engines, gas turbines, nuclear reactors, re-entry vehicles, and hypersonic structures are subject to severe environmental conditions that present significant challenges. With their remarkable properties, such as high melting temperatures, strong resistance to oxidation, corrosion, and ablation, minimal creep, and advantageous thermal cycling behavior, ceramic matrix composites (CMCs) show great promise as a material to meet the strict requirements in these kinds of environments. Furthermore, the addition of boron nitride nanoparticles with continuous fibers to the CMCs can offer thermal resistivity in harsh conditions, which will improve the composites’ strength and fracture toughness. Therefore, in extreme situations, it is crucial to understand the thermal resistivity period of composite materials. To forecast the ablation performance of composites, we developed six machine learning regression methods in this study: decision tree, random forest, support vector machine, gradient boosting, extreme gradient boosting, and adaptive boosting. When evaluating model performance using metrics including R2 score, root mean square error, mean absolute error, and mean absolute percentage error, the gradient boosting and extreme gradient boosting machine learning regression models performed better than the others. The effectiveness of machine learning models as a useful tool for forecasting the ablation behavior of ceramic matrix composites was effectively explained by this study.