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arXiv 2406.16166cond-mat.mtrl-scics.LG
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基于机器学习的断裂韧性优化复合材料设计

Composite Material Design for Optimized Fracture Toughness Using Machine Learning

University of Central Florida(中佛罗里达大学);Swinburne University of Technology(斯威本科技大学)

Mohammad Naqizadeh Jahromi, Mohammad Ravandi

AI总结:

本研究利用机器学习技术优化二维与三维复合材料结构的断裂韧性,针对DCB试验的四类工况验证了其在有限数据下快速探索设计空间、预测力学行为的优势,提升了复合材料设计效率。

摘要:

本文采用机器学习(ML)技术研究二维和三维复合结构的优化问题,重点关注双悬臂梁(DCB)试验中的断裂韧性与裂纹扩展行为。通过探究复合材料微观结构排布与宏观性能之间的复杂关联,本研究证明了机器学习作为加速设计优化流程的有力工具的潜力,相较于传统有限元分析具备显著优势。本研究涵盖四类不同工况,分别考察了二维与三维复合材料模型中的裂纹扩展与断裂韧性特性。通过应用机器学习算法,研究展示了其快速、精准地探索复合材料庞大设计空间的能力。研究结果凸显了机器学习在有限训练数据下预测力学行为的高效性,为其在复合材料设计与优化领域的更广泛应用铺平了道路。本工作有助于深化对机器学习在提升复合材料设计流程效率方面作用的理解。

英文摘要:

This paper investigates the optimization of 2D and 3D composite structures using machine learning (ML) techniques, focusing on fracture toughness and crack propagation in the Double Cantilever Beam (DCB) test. By exploring the intricate relationship between microstructural arrangements and macroscopic properties of composites, the study demonstrates the potential of ML as a powerful tool to expedite the design optimization process, offering notable advantages over traditional finite element analysis. The research encompasses four distinct cases, examining crack propagation and fracture toughness in both 2D and 3D composite models. Through the application of ML algorithms, the study showcases the capability for rapid and accurate exploration of vast design spaces in composite materials. The findings highlight the efficiency of ML in predicting mechanical behaviors with limited training data, paving the way for broader applications in composite design and optimization. This work contributes to advancing the understanding of ML's role in enhancing the efficiency of composite material design processes.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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