基于深度学习的平纹Cf/SiC复合材料原位拉伸损伤演化与断裂分析

为揭示平纹C_f/SiC复合材料的拉伸损伤演化及失效机理,开展了X射线CT原位拉伸试验,获得材料的三维重构图像,利用深度学习的图像分割方法,准确识别出拉伸裂纹并实现其三维可视化。分析了平纹C_f/SiC复合材料损伤演化与失效机理,基于裂纹的三维可视化结果对材料损伤进行了定量表征。结果表明:平纹C_f/SiC复合材料的拉伸力学行为呈现非线性,拉伸过程中主要出现基体开裂、界面脱黏、纤维断裂及纤维拔出等损伤;初始缺陷易引起材料损伤,孔隙多的部位裂纹数量也多;纤维束外基体裂纹可扩展至纤维束内部,并发生裂纹偏转。基于深度学习的智能图像分割方法为定量评估陶瓷基复合材料损伤演化与失效机理提供了有效分析手段。

In-Situ Tensile Damage Evolution and Fracture Analysis of Plain Weave Cf/SiC Composites Based on Deep Learning

In order to reveal the tensile damage evolution and failure mechanism of plain weave C_f/SiC composites,the X-ray CT in-situ tensile test was carried out to obtain the three-dimensional reconstructed image of the material.The deep learning based image segmentation method was used to accurately identify the tensile crack and realize its three-dimensional visualization.The damage evolution and failure mechanism of plain weave C_f/SiC composites were analyzed,and the damage was quantitatively characterized based on the three-dimensional visualization results.The results show that the tensile mechanical behavior of plain weave C_f/SiC composites is nonlinear,and damages such as matrix cracking,interface debonding,fiber fracture,and fiber pull-out occur during the tensile process.The initial defects are easy to cause material damage,and the higher the porosity,the more cracks there are.The matrix crack outside the fiber tow can extend to the inside of the fiber tow and the crack deflection occurs.The deep learning based intelligence image segmentation method provides an effective analysis method to quantitatively evaluate the damage evolution and failure mechanism of ceramic matrix composites.