高抗烧蚀改性C/SiC复合材料:数据驱动的智能设计与实验验证

IF 3.8 3区 材料科学 Q1 MATERIALS SCIENCE, CERAMICS
Zirui Du, Chaokun Song, Kang Guan, Pinggen Rao, Xiaohui Yang, Longteng Bai, Shengbo Shi, Jing Wang, Yongsheng Liu, Qingfeng Zeng
{"title":"高抗烧蚀改性C/SiC复合材料:数据驱动的智能设计与实验验证","authors":"Zirui Du,&nbsp;Chaokun Song,&nbsp;Kang Guan,&nbsp;Pinggen Rao,&nbsp;Xiaohui Yang,&nbsp;Longteng Bai,&nbsp;Shengbo Shi,&nbsp;Jing Wang,&nbsp;Yongsheng Liu,&nbsp;Qingfeng Zeng","doi":"10.1111/jace.70209","DOIUrl":null,"url":null,"abstract":"<p>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 &lt; ∼0.075, mean squared error &lt; ∼0.015, and coefficient of determination [R<sup>2</sup>] &gt; ∼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<sub>2</sub>-modified C/SiC composites with exceptional ablation resistance. The optimized composite, containing approximately 20.0 vol.% ZrB<sub>2</sub>, 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<sub>2</sub>‒B<sub>2</sub>O<sub>3</sub> 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.</p>","PeriodicalId":200,"journal":{"name":"Journal of the American Ceramic Society","volume":"108 12","pages":""},"PeriodicalIF":3.8000,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Modified C/SiC composites with high ablation resistance: Data-driven intelligent design and experimental validation\",\"authors\":\"Zirui Du,&nbsp;Chaokun Song,&nbsp;Kang Guan,&nbsp;Pinggen Rao,&nbsp;Xiaohui Yang,&nbsp;Longteng Bai,&nbsp;Shengbo Shi,&nbsp;Jing Wang,&nbsp;Yongsheng Liu,&nbsp;Qingfeng Zeng\",\"doi\":\"10.1111/jace.70209\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>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 &lt; ∼0.075, mean squared error &lt; ∼0.015, and coefficient of determination [R<sup>2</sup>] &gt; ∼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<sub>2</sub>-modified C/SiC composites with exceptional ablation resistance. The optimized composite, containing approximately 20.0 vol.% ZrB<sub>2</sub>, 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<sub>2</sub>‒B<sub>2</sub>O<sub>3</sub> 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.</p>\",\"PeriodicalId\":200,\"journal\":{\"name\":\"Journal of the American Ceramic Society\",\"volume\":\"108 12\",\"pages\":\"\"},\"PeriodicalIF\":3.8000,\"publicationDate\":\"2025-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of the American Ceramic Society\",\"FirstCategoryId\":\"88\",\"ListUrlMain\":\"https://ceramics.onlinelibrary.wiley.com/doi/10.1111/jace.70209\",\"RegionNum\":3,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MATERIALS SCIENCE, CERAMICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of the American Ceramic Society","FirstCategoryId":"88","ListUrlMain":"https://ceramics.onlinelibrary.wiley.com/doi/10.1111/jace.70209","RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATERIALS SCIENCE, CERAMICS","Score":null,"Total":0}
引用次数: 0

摘要

提高碳纤维增强碳化硅(C/SiC)复合材料的抗烧蚀性对于满足超高温应用的严格要求至关重要,但传统的经验设计方法是资源密集且耗时的。本研究提出了一种新的数据驱动方法,将机器学习(ML)与实验验证相结合,以优化C/SiC复合材料的抗烧蚀性。通过对102个实验样本的系统数据预处理和特征分析,建立了XGBoost回归模型,获得了令人满意的烧蚀率预测精度(平均绝对误差<; ~ 0.075,均方误差<; ~ 0.015,决定系数[R2] >; ~ 0.75)。SHapley添加剂解释(SHAP)分析表明,改性剂的性能,特别是标准焓和熔点,主要影响抗烧蚀性。通过贝叶斯优化实现机器学习指导设计策略,成功制备了具有优异抗烧蚀性能的zrb2改性C/SiC复合材料。优化后的复合材料含有约20.0 vol.%的ZrB2,在氧乙炔炬测试中实现了2.083µm/s的线性烧蚀速率,比传统的复合材料有了显著的提高。显微组织分析证实了致密SiO2-B2O3保护层的形成,验证了预测的抗烧蚀增强机理。这项工作为加速超高温陶瓷的发展建立了一个强大的框架,并证明了机器学习驱动方法在材料设计优化中的有效性。开发了一个具有交互式图形用户界面的面向对象软件。这些方法已经集成到一个交互式软件中,即Modified C/SiC烧蚀率智能设计软件(MARS),为具有定制烧蚀性能的C/SiC复合材料的加速设计创造了一个有效的工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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

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

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.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Journal of the American Ceramic Society
Journal of the American Ceramic Society 工程技术-材料科学:硅酸盐
CiteScore
7.50
自引率
7.70%
发文量
590
审稿时长
2.1 months
期刊介绍: The Journal of the American Ceramic Society contains records of original research that provide insight into or describe the science of ceramic and glass materials and composites based on ceramics and glasses. These papers include reports on discovery, characterization, and analysis of new inorganic, non-metallic materials; synthesis methods; phase relationships; processing approaches; microstructure-property relationships; and functionalities. Of great interest are works that support understanding founded on fundamental principles using experimental, theoretical, or computational methods or combinations of those approaches. All the published papers must be of enduring value and relevant to the science of ceramics and glasses or composites based on those materials. Papers on fundamental ceramic and glass science are welcome including those in the following areas: Enabling materials for grand challenges[...] Materials design, selection, synthesis and processing methods[...] Characterization of compositions, structures, defects, and properties along with new methods [...] Mechanisms, Theory, Modeling, and Simulation[...] JACerS accepts submissions of full-length Articles reporting original research, in-depth Feature Articles, Reviews of the state-of-the-art with compelling analysis, and Rapid Communications which are short papers with sufficient novelty or impact to justify swift publication.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信