Computational solid mechanics has become an indispensable approach in
engineering, and numerical investigation of fracture in composites is essential
as composites are widely used in structural applications. Crack evolution in
composites is the bridge to elucidate the relationship between the
microstructure and fracture performance, but crack-based finite element methods
are computationally expensive and time-consuming, limiting their application in
computation-intensive scenarios. Here we propose a deep learning framework
called Crack-Net, which incorporates the relationship between crack evolution
and stress response to predict the fracture process in composites. Trained on a
high-precision fracture development dataset generated using the phase field
method, Crack-Net demonstrates a remarkable capability to accurately forecast
the long-term evolution of crack growth patterns and the stress-strain curve
for a given composite design. The Crack-Net captures the essential principle of
crack growth, which enables it to handle more complex microstructures such as
binary co-continuous structures. Moreover, transfer learning is adopted to
further improve the generalization ability of Crack-Net for composite materials
with reinforcements of different strengths. The proposed Crack-Net holds great
promise for practical applications in engineering and materials science, in
which accurate and efficient fracture prediction is crucial for optimizing
material performance and microstructural design.

提出了一种称为 Crack-Net 的深度学习框架，它通过结合裂纹演化与应力响应之间的关系来预测复合材料的断裂过程。通过在高精度裂纹发展数据集上进行训练，Crack-Net 展示了准确预测给定复合材料设计的裂纹生长模式和应力 - 应变曲线的显著能力。Crack-Net 捕捉了裂纹生长的基本原理，使其能处理更复杂的微结构，并采用迁移学习进一步提高了对不同强度增强材料的泛化能力。这种提出的 Crack-Net 在工程和材料科学的实际应用中具有巨大的潜力，其中准确和高效的断裂预测对于优化材料性能和微观结构设计至关重要。