Research On Infrared Nondestructive Testing And Defect Quantification Of Composite Materials

Due to its excellent mechanical properties,composite materials have been widely used in various fields.However,the production,transportation and use of composite materials will inevitably be affected by random factors,leading to material failure,and even serious safety accidents.Therefore,it is of great significance to carry out effective detection and evaluation of the health status of composite material components.Infrared thermal wave non-destructive testing technology,as a non-contact testing technology,has many characteristics such as convenience,intuitiveness and wide range of applications.It has been developed rapidly in the past 30 years and is widely used in aerospace,military,new materials,nuclear power systems and other fields.Infrared thermal wave detection technology essentially expresses the internal state information of the composite material excited by the thermal wave through the surface temperature field distribution,and records it with an infrared detector,expands it into a thermal map sequence in the time dimension,and then use the thermal image sequence data analysis method to reveal the defect information.Therefore,it is very suitable for detection and analysis of composite materials.Based on the above analysis,in this thesis,combining the characteristics of composite materials,the problem of image processing and defect quantitative analysis in nondestructive testing of composite materials is studied.In terms of model abstraction and data preprocessing,a reasonable heat wave conduction model for the general composite material flat specimen is established in this thesis,which lays a mathematical foundation for subsequent research.Combining the characteristics of infrared thermal wave images,in the image preprocessing stage,the time dimension and space dimension are considered at the same time,combined with the Thermographic Signal Reconstruction(TSR)technology,a background removal algorithm based on the Trust Region Reflective(TRR)algorithm is proposed to complete the removal of outlier data and uneven thermal image background;In order to synthesize defect information of different depths,the kernel Principal Component Analysis(PCA)technology is adopted to extract the defect signals,which further increases the defect signal-to-noise ratio of the thermal wave image to 14.54.In terms of image segmentation,in order to achieve the rapid segmentation of defects,a two-dimensional Tsallis cross entropy threshold segmentation method is proposed,and the bat algorithm based on chaotic mapping is integrated into the algorithm to improve the efficiency of the segmentation algorithm.As for precise segmentation of defects,a new level set energy functional segmentation model is proposed.This model combines the characteristics of fuzzy clustering,Shannon entropy and point spread function to further improve the robustness of the algorithm.In terms of quantification of defects,based on the precise segmentation results,the defects are quantitatively studied.On the issue of defect area and location estimation,the discrete Green's formula is adopted,and the center coordinates of the defect and the pixel area are calculated at the same time.Aiming at the problem of quantitatively estimating the depth of defects,the virtual heat source method is adopted to extend the traditional one-dimensional heat wave conduction equation to two-dimensional in the cylindrical coordinate system,thereby,the previous model was revised to accurately reflect the depth information of the defect.The experimental results show that the defect depth estimation error can be controlled within 9% by the proposed model.