Thermographic clustering analysis for defect detection in CFRP structures

Zheng, Kaiyi; Chang, Yu-Sung; Wang, Kaihong; Yao, Yuan

doi:10.1016/j.polymertesting.2015.11.009

Cited by 26 publications

(11 citation statements)

References 24 publications

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“…In paper [11] cluster analysis (TCA), a method for automatic defect detection is based on three-dimensional image segmentation. Many fuzzy, C-Means, K-Means clustering methods are described in the papers [12][13][14][15]. These approaches are widely used for image segmentation, pattern recognition, finding the optimal segmentation threshold, classification and defect detection.…”

Section: Introductionmentioning

confidence: 99%

Measurement of material surface defect intensity by distributed cumulative histogram and clustering

Melnyk¹,

Kvit²

2020

TAPR

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The object of research is a distributed cumulative histogram of a digital image and its advantages for automated determination of the location and intensity of defects of different nature on the surfaces of materials: metal, paper, etc. The technique considered in the study is aimed at minimization of human interference in the process of material surface control from the moment of its photographing to the moment of making a decision about the surface quality. Three-dimensional distributed cumulative histogram (DCH) is presented as a two-dimensional image in which the pixel intensity corresponds to the third dimension-the number of pixels of a certain intensity in the original surface image. Informative distributed cumulative histogram (IDCH) is used to recognize black, dark and light defects, and to measure their intensity and location by the clustering algorithm. The average value of the pixel intensity in the columns and rows of the pixel matrix of the cumulative histogram image is calculated to estimate the intensity of the defects. Measurement of the intensity of defects is carried out in two ways: directly on the image of the surface sample and by comparing the image of the sample and the reference image of the sample without defects. To solve the problem, an algorithm of hierarchical clustering of data to rectangular segments of the surface image is used. In the image, each cluster is marked with a corresponding color of gray. The image for analysis is transformed using segmentation and inversion algorithms. This allows to get more accurate estimates of the intensity of light and dark defects. The clustering algorithm groups the image segments of the surface samples, as well as the images of the distributed cumulative histogram to group the level of surface damage. Distributed cumulative histogram was used to detect defects on the surface of materials as a method of linking the number and intensity of pixels to image coordinates. Cluster analysis helps to find their coordinates and intensity. In comparison with known approaches, the method has a linear algorithmic complexity to the number of pixels in the input image, which allows to do a significant number of experiments to identify the types of surfaces of materials for use and the features of algorithms.

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Section: Introductionmentioning

confidence: 99%

Measurement of material surface defect intensity by distributed cumulative histogram and clustering

Melnyk¹,

Kvit²

2020

TAPR

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“…Yousefi B et al [26] combine sparse principal component analysis (SPCA) with K-means clustering to achieve automatic segmentation of defects. In order to avoid the possibility of losing necessary defect information, Zheng K et al [27][28] analysis all the thermographic data. The thermographic cluster analysis (TCA) method and hyper-image segmentation method can automatically segment the shape of the defect.…”

Section: Introductionmentioning

confidence: 99%

Temporal and spatial deep learning network for infrared thermal defect detection

Luo

Gao

Woo

et al. 2019

NDT & E International

127

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Most common types of defects for composite are debond and delamination. It is difficult to detect the inner defects on a complex shaped specimen by using conventional optical thermography nondestructive testing (NDT) methods. In this paper, a hybrid of spatial and temporal deep learning architecture for automatic thermography defects detection is proposed. The integration of cross network learning strategy has the capability to significantly minimize the uneven illumination and enhance the detection rate. 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. The results show that visual geometry group-Unet (VGG-Unet) cross learning structure can significantly improve the contrast between the defective and non-defective regions. In addition, investigation of different feature extraction methods in which embedded in deep learning is conducted to optimize the learning structure. To investigate the efficacy and robustness of the proposed method, experimental studies have been carried out for inner debond defects on both regular and irregular shaped carbon fiber reinforced polymer (CFRP) specimens.

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“…Many of the same techniques used for image or video analysis and computer vision can also be applied to thermographic data. Zheng focused on image segmentation and minimum spanning tree clustering to automate the identification and localization of defects in thermal data [12].…”

Section: Introductionmentioning

confidence: 99%

In-situ thermography of automated fiber placement parts

Gregory¹,

Juarez²

2018

AIP Conference Proceedings

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Abstract. Automated fiber placement (AFP) provides precision and repeatable manufacturing of both simple and complex geometry composite parts. However, AFP also introduces the possibility for unique flaws such as overlapping tows, gaps between tows, tow twists, lack of layer adhesion and foreign object debris. These types of flaws can all result in a significant loss of performance in the final part. The current inspection method for these flaws is a costly and time intensive visual inspection of each ply layer. This work describes some initial efforts to incorporate thermal inspection on the AFP head and analysis of the data to identify the previously mentioned flaws. Previous bench-top laboratory experiments demonstrated that laps, gaps, and twists were identified from a thermal image. The AFP head uses an onboard lamp to preheat the surface of the part during layup to increase ply consolidation. The preheated surface is used as a thermal source to observe the state of the new material after compaction. We will present data collected with the Integrated Structural Assembly of Advanced Composites (ISAAC) AFP machine at Langley Research Center showing that changes to the temperature profile is sufficient for identifying all types of flaws.

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Thermographic clustering analysis for defect detection in CFRP structures

Cited by 26 publications

References 24 publications

Measurement of material surface defect intensity by distributed cumulative histogram and clustering

Measurement of material surface defect intensity by distributed cumulative histogram and clustering

Temporal and spatial deep learning network for infrared thermal defect detection

In-situ thermography of automated fiber placement parts

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