Comparison of machine learning methods and finite element analysis on the fracture behavior of polymer composites

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.

Automated summary

Recent research has used experimental methods, finite element analysis, and machine learning algorithms to analyze the fracture behavior of polymer composites. The results show good agreement between these methods in analyzing fracture behavior.