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.

通过机器学习技术优化 2D 和 3D 复合结构，研究断裂韧性和裂纹扩展在双悬臂梁（DCB）试验中的应用，揭示微观结构排列与复合材料宏观性能之间的复杂关系，证明了机器学习作为强大工具在设计优化过程中的潜力，相较于传统有限元分析具有明显优势。研究包含四个不同案例，分别检查 2D 和 3D 复合模型中的裂纹扩展和断裂韧性。通过应用机器学习算法，研究展示了在复合材料设计中对庞大设计空间进行快速准确探索的能力。研究结果强调了机器学习在有限训练数据下预测机械行为的效率，为复合材料设计和优化的广泛应用铺平了道路。该研究对提高复合材料设计过程中机器学习的效率做出了贡献。