High resolution detection and recognition method for debonding defect of composite material

Abstract

The present invention discloses a high resolution detection and recognition method for a debonding defect of a composite material. A sample with a defect is under the effect of an infrared thermal imaging system, the thermal diffusion and thermal radiation properties of a material are different at different areas of the material, the defect has a high temperature area, the surface temperature field collected by a thermal imager can reflect certain defect information with time and space transform, information is provided for a later feature extraction algorithm, and the information of the debonding defect of the composite material is extracted based on an integrated variational Bayesian tensor decomposition algorithm.

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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