Research On Defects Detection For CFRP/Al Honeycomb Based On Code Modulation Infrared Thermal Imaging And 3D Matched Filtering

CFRP/Al honeycomb composites are widely used in aerospace,high-speed trains,energy and chemical industries due to their light weight,high specific strength,good stability,corrosion resistance,sound and heat insulation,etc.The preparation process of CFRP/Al honeycomb structure composites is relatively complex,and the service environment is harsh,which is prone to internal defects such as debonding,water accumulation and collapse.Infrared thermal wave nondestructive testing technology has the advantages of non-contact,intuitive and efficient,which provides a reliable method for the detection of defects in CFRP/Al honeycomb structure.In this paper,the key technologies of CFRP/Al honeycomb structure defect detection,such as Barker code modulation infrared thermal wave detection principle analysis and simulation research,Barker code modulation infrared thermal wave detection system construction,defect size and type exploration,infrared image sequence and image processing algorithm,are studied.The heat flow transfer process of Barker code modulated excitation CFRP/Al honeycomb structure specimens is analyzed,and a one-dimensional analytical model of surface temperature field distribution is established.The effects and laws of defect geometric features and types on surface temperature signals are analyzed through simulation,and the detection capability of Barker code modulated excitation infrared thermography for detecting defects in CFRP/Al honeycomb structures is explored.An infrared thermal imaging nondestructive testing system with halogen lamp excitation is built and realized the efficient and reliable detection of defects in CFRP/Al honeycomb structure.Through experimental research,the influence and law of geometric features and types of defects on the surface temperature signal of the specimen are analyzed,the correctness of the theoretical model is verified,and the detection effect is compared and analyzed with traditional methods such as pulse excitation and sinusoidal excitation.The Barker coding modulation excitation infrared image sequence matching filtering algorithm is studied,and four feature matching filter operators are analyzed to perform targeted processing and temperature signal feature extraction on infrared thermogram sequences,and the differences between different algorithms in terms of SNR(signal-to-noise ratio)and defect detection capability are comprehensively evaluated,the enhancement of the experimentally obtained infrared images is realized by histogram enhancement,contrast enhancement and filtering,etc.,Through iterative threshold segmentation,Otsu threshold segmentation,watershed algorithm and multi structure morphology PCNN hybrid algorithm,infrared image segmentation is realized.based on classical edge detection operators and genetic algorithms,defect edge detection of IR images of honeycomb structures is achieved,and the edge detection effects of different algorithms are compared and analyzed.