Quantitative Surface Crack Evaluation Based on Eddy Current Pulsed Thermography

Eddy current pulsed thermography (ECPT) is an emerging non-destructive testing technology and has an increasing span of application with capabilities of rapid contactless and large surface area detection. The close process loop of ECPT that contains pre-processing, post-processing, and objective quantitative assessment is rarely presented. This paper proposed a complete strategy aims to perform pre- and post- processing for surface crack detection based on ECPT platform. In addition, the quantitative evaluation is involved to objectively evaluate detectability. Specially, a new post-image segmentation algorithm is proposed which based on the idea of grouping histogram and iterative adaption approach that leads to better performance for quantitatively identifying and sizing the defect. Experimental tests on man-made metal and natural defects have been conducted to show the reliability of the proposed strategy. This paper can be further applied for other types of defects detection, quantitative evaluation, and aid in the development of machine vision industry for automated visual inspection

Automatic seeded region growing for thermography debonding detection of CFRP

The carbon fiber reinforced polymer (CFRP) has been widely used in aerospace, automobile and sports industries. In laminated composite materials, cyclic stresses and impact will cause internal defects such as delamination and debonding. In order to guarantee internal quality and safety, optical pulsed thermography (OPT) nondestructive testing has been used to detect the internal defects. However, current OPT methods cannot efficiently tackle the influence from uneven illumination, and the resolution enhancement of the defects detection remains as a critical challenge. In this paper, a hybrid of thermographic signal reconstruction (TSR) and automatic seeded region growing (ASRG) algorithm is proposed to deal with the thermography processing of CFRP. The proposed method has the capability to significantly minimize uneven illumination and enhance the detection rate. In addition, it has the capacity to automate segmentation of defects. It also overcomes the crux issues of seeded region growing (SRG) which can automatically select the growth of image, seed points and thresholds. The probability of detection (POD) has been derived to measure the detection results and this is coupled with comparison studies to verify the efficacy of the proposed method

Ensemble Bayesian Tensor Factorization for Debond Thermal NDT

One of the common types of defects in the carbon fiber reinforced polymer (CFRP) is debond. The different feature extraction algorithms of optical stimulated infrared thermography are used to obtained the debond detection. However, the low detection accuracy as well as remain as challenges. In this paper, the ensemble variational Bayes tensor factorization (EVBTF) has been proposed to overcome the problems. The framework of the proposed algorithm is based on the Bayesian learning theory. It constructs spatial-transient multi-layer mining structure. Experimental tests have been proved that it can effectively improve the contrast ratio between the defective areas and the sound areas