Research progress of hole-making defect detection technology for aerospace composite materials

Carbon fiber reinforced polymer (CFRP) has significant application value in the aerospace field due to its lightweight and high-strength characteristics. However, during the hole-making process, the material’s anisotropic nature makes it susceptible to defects such as delamination and burrs, which can adversely affect the service performance and assembly quality of components. Therefore, it is necessary to conduct high-precision and high-efficiency detection on it. Based on the analysis of the defect mechanisms associated with hole-making in CFRP, this paper thoroughly examines the applicability of non-destructive testing technologies for CFRP hole-making defect. It analyzes the characteristics of traditional machine vision detection methods with those based on deep learning techniques. Furthermore, it emphasizes that intelligent detection technology utilizing multimodal data fusion provides significant advantages in enhancing both detection accuracy and efficiency. In addition, in response to the challenges currently faced by CFRP hole-making defect detection technologies, it proposes several development paths. These include the creation of an anisotropic adaptive detection algorithm, the establishment of a standardized defect classification system, and the implementation of online monitoring for the hole-making process. The aim is to provide innovative theoretical support and technical direction for intelligent and high-precision detection in the field of aerospace composite material manufacturing.