In recent years, it became possible to use different methods for the analysis of mechanical systems with the help of computers to learn like humans and by increasing their interaction with the world by observing autonomously. One of these mechanical analyzes is the fracture mechanics in which the behavior of the laminated composites having a crack is examined. In this study, experimental methods, finite element analysis (FEA) and machine learning algorithms (MLA) were used to analyze the fracture behavior of polymer composites in Mode I, Mode I/II and Mode II loading situations. For the experimental study, the fracture behaviors of the laminated composites reinforced with pure glass, pure carbon and glass/carbon hybrid knitted fabrics were tested with the help of Arcan test apparatus. In the finite element method, the linear elastic fracture behavior at the crack tip was analyzed by using the J-integral method. In the field of MLA, there is no single learning algorithm that provides good learning on all real-world problem data. Therefore, algorithm selection is done experimentally so various machine algorithms were used in the study. The analysis result showed that the finite element analysis and machine learning results were in good agreement with experimental measurements. This study is particularly important for the comparison of machine learning techniques with FEA in regression applications.

近年来，在计算机的帮助下，可以使用不同的方法来分析机械系统，从而像人一样学习，并通过自主观察来增加它们与世界的互动。这些力学分析之一是断裂力学，其中检查了具有裂纹的层压复合材料的行为。在这项研究中，实验方法，有限元分析（FEA）和机器学习算法（MLA）用于分析聚合物复合材料在模式I，模式I / II和模式II加载情况下的断裂行为。为了进行实验研究，借助Arcan测试设备测试了用纯玻璃，纯碳和玻璃/碳混合编织物增强的层压复合材料的断裂行为。在有限元法中 用J积分法分析了裂纹尖端的线性弹性断裂行为。在MLA领域，没有一种学习算法可以对所有现实问题数据提供良好的学习。因此，算法选择是通过实验完成的，因此在研究中使用了各种机器算法。分析结果表明，有限元分析和机器学习结果与实验测量结果吻合良好。对于在回归应用中将机器学习技术与FEA进行比较而言，这项研究特别重要。算法选择是通过实验完成的，因此研究中使用了各种机器算法。分析结果表明，有限元分析和机器学习结果与实验测量结果吻合良好。对于在回归应用中将机器学习技术与FEA进行比较而言，这项研究特别重要。算法选择是通过实验完成的，因此研究中使用了各种机器算法。分析结果表明，有限元分析和机器学习结果与实验测量结果吻合良好。对于在回归应用中将机器学习技术与FEA进行比较而言，这项研究特别重要。