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RESEARCH PAPER · Ceramics International · 2022

Exploration of the oxidation and ablation resistance of ultra-high-temperature ceramic coatings using machine learning

Jie Hao, Lihong Gao, Zhuang Ma, Yanbo Liu, Ling Liu, Shizhen Zhu, Weizhi Tian, Xiaoyu Liu, Zhigang Zhou, Alexandr Alexandrovich Rogachev, Hanyang Liu

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Exploration of the oxidation and ablation resistance of ultra-high-temperature ceramic coatings using machine learningOriginalIntegration of machine learning models and metaheuristic algorithms for predicting compressive strength of waste granite powder concrete15Accelerated discovery of oxidation-resistant ultra-high temperature ceramics via data driven methodology11Thermal conductivity prediction of sintered reaction bonded silicon nitride ceramics using a machine learning approach based on process conditions19A Comparative Analysis of Machine Learning Techniques for Predicting the Wear Rate of Ceramic Coated Steel16Exploration of high-temperature oxidation resistance laws in ultra-high temperature boride ceramics through data-driven approaches17Multilayer artificial intelligence for thermal-conductivity prediction of silicon nitride ceramics from powder processing conditions and predicted densities7Machine learning-enabled prediction of high-temperature oxidation resistance for Ni-based alloys29Analysis and regularity of ablation resistance performance of ultra-high temperature ceramic matrix composites using data-driven strategy13Review of progress in calculation and simulation of high-temperature oxidation181Predicting surface roughness of carbon/phenolic composites in extreme environments using machine learning5Construction of Prediction Models of Mass Ablation Rate for Silicone Rubber-Based Flexible Ablative Composites Based on a Small Dataset1Data‐Driven Materials Research and Development for Functional Coatings120High‐temperature ablation resistance prediction of ceramic coatings using machine learning101Microstructural basis of AI predictions for material properties: A case study of silicon nitride ceramics using t ‐SNE8Evaluating the Thermal Shock Resistance of SiC-C/CA Composites Through the Cohesive Finite Element Method and Machine Learning3Ablation and mechanical properties of carbon/high silica/phenolic composites: Using experimental analysis and five machine learning models for correlating and generalizing to diverse compositional ratios9Thermal Stability of Residual Stress, Microstructure, and Mechanical Property in Shot-Peened CNT/Al-Cu-Mg Composites5Materials design of silicon based ceramic coatings for high temperature oxidation protection175Data-driven assisted design and quantitative prediction of hardness in high-entropy boride ceramics3Predicting the hardness of diamond-like carbon coatings using machine learning and generative adversarial networks17Accelerated discovery of Al-Zn-Mg-Cu alloys with high-strength and high-plasticity by machine learning3Multiscale structure-mechanical property relationship and data-driven design of ultra-high temperature ceramics: A review7Efficient optimization of ultra-high temperature ceramic oxidation resistance based on limited data via GAN-enhanced active learning5Advances in ultra-high-temperature ceramic coatings with enhanced oxidation resistance for carbon-based composites19Modified C/SiC composites with high ablation resistance: Data‐driven intelligent design and experimental validation4Multi-functional design of ultra-high temperature ceramics coatings7Self-healing ceramic coatings: Mechanisms, design strategies, and emerging applications for extreme environments11SHAP enhanced machine learning prediction of structure–property relationships in plasma sprayed lanthanum zirconate coatings for TBC applications2Influence of SiC molar ratio on ablation resistance in TaC-ZrC-SiC multi-component composite coatings0Machine learning guided design and ablation behavior of ZrC-TaC-SiC ternary coatings90Accelerating multi-objective Bayesian optimization of ablation resistance and hardness in ultra-high-temperature ceramics via data augmentation-assisted dual-model prediction using generative adversarial networks1Interpretable machine learning driven design of oxidation resistant Co-based superalloys with low density0Research Progress and Prospects of Ultra-High-Temperature Ceramics: Experimentation, Multiscale Simulation and Data-Driven Design3A hybrid physics-informed neural network and gradient boosting tree approach for predicting earthquake-induced pipeline failure probability1Damage failure process prediction of laser protective coatings based on finite element simulation and deep learning1Fundamentals of Thermal Transport and Energy Conversion in Ultra‐High Temperature Ceramics: From Microscale Mechanisms to Macroscopic Properties0

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  4. A Comparative Analysis of Machine Learning Techniques for Predicting the Wear Rate of Ceramic Coated Steel

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    Corrosion Science · 2024-05-18

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  8. Analysis and regularity of ablation resistance performance of ultra-high temperature ceramic matrix composites using data-driven strategy

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  11. Construction of Prediction Models of Mass Ablation Rate for Silicone Rubber-Based Flexible Ablative Composites Based on a Small Dataset

    Applied Sciences · 2024-09-07

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  14. Microstructural basis of AI predictions for material properties: A case study of silicon nitride ceramics using t ‐SNE

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  15. Evaluating the Thermal Shock Resistance of SiC-C/CA Composites Through the Cohesive Finite Element Method and Machine Learning

    Applied Sciences · 2024-11-27

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  16. Ablation and mechanical properties of carbon/high silica/phenolic composites: Using experimental analysis and five machine learning models for correlating and generalizing to diverse compositional ratios

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  17. Thermal Stability of Residual Stress, Microstructure, and Mechanical Property in Shot-Peened CNT/Al-Cu-Mg Composites

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  18. Materials design of silicon based ceramic coatings for high temperature oxidation protection

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  19. Data-driven assisted design and quantitative prediction of hardness in high-entropy boride ceramics

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  20. Predicting the hardness of diamond-like carbon coatings using machine learning and generative adversarial networks

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  21. Accelerated discovery of Al-Zn-Mg-Cu alloys with high-strength and high-plasticity by machine learning

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  22. Multiscale structure-mechanical property relationship and data-driven design of ultra-high temperature ceramics: A review

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  23. Efficient optimization of ultra-high temperature ceramic oxidation resistance based on limited data via GAN-enhanced active learning

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  24. Advances in ultra-high-temperature ceramic coatings with enhanced oxidation resistance for carbon-based composites

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

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