A high-resolution detection and identification method for debonding defects in composite materials

Abstract

The invention discloses a high-resolution detection and identification method for debonding defects of composite materials. Under the action of a light-excited infrared thermal imaging system, a sample containing defects has different thermal diffusion and thermal radiation characteristics in different regions of the material. There is a high-temperature area at the defect, and the surface temperature field collected by the thermal imager can reflect certain defect information with time and space, and provide information for the later feature extraction algorithm, and then based on the integrated variational Bayesian tensor decomposition algorithm Extracting information on debonding defects in composite materials.

Description

technical field [0001] The invention belongs to the technical field of non-destructive testing and evaluation, and more specifically relates to a high-resolution detection and identification method for debonding defects of composite materials. Background technique [0002] Due to the advantages of high specific strength, large specific modulus, good fatigue resistance, low expansion coefficient and strong designability, carbon fiber reinforced composites (CFRP) have become indispensable and important structural materials in high-tech fields such as aerospace. However, damage is unavoidable in the process of manufacturing, assembly, long-term service, etc. Common internal defects of composite composite structures include delamination, debonding, inclusions, etc. Since the deformation of the internal structure is difficult to detect and causes great damage, With the continuous improvement of processing technology and application requirements of composite materials, higher requ...

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Problems solved by technology

[0003] However, the image obtained by the optical excitation infrared thermal imaging system is affected by the thermal imager and the lateral thermal diffusion of the composite material, and has the disadvantages of low resolution, blurred edges, and noise interference. In order to improve the accuracy and efficiency of defect detection, many feature extraction Algorithms are used to extract image defect information, such as thermal imaging signal reconstruction (TSR), principal component analysis (PCA) and pulse phase thermal imaging (PPT), etc.

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