Infrared Thermography for Weld Inspection: Feasibility and Application

Traditional ultrasonic testing (UT) techniques have been widely used to detect surface and sub-surface defects of welds. UT inspection is a contact method which burdens the manufacturer by storing hot specimens for inspection when the material is cool. Additionally, UT is only valid for 5 mm specimens or thicker and requires a highly skilled operator to perform the inspections and interpret the signals. Infrared thermography (IRT) has the potential to be implemented for weld inspections due to its non-contact nature. In this study, the feasibility of using IRT to overcome the limitations of UT inspection is investigated to detect inclusion, porosity, cracking, and lack of fusion in 38 weld specimens with thicknesses of 3, 8 and 13 mm. UT inspection was also performed to locate regions containing defects in the 8 mm and 13 mm specimens. Results showed that regions diagnosed with defects by the UT inspection lost heat faster than the sound weld. The IRT method was applied to six 3 mm specimens to detect their defects and successfully detected lack of fusion in one of them. All specimens were cut at the locations indicated by UT and IRT methods which proved the presence of a defect in 86% of the specimens. Despite the agreement with the UT inspection, the proposed IRT method had limited success in locating the defects in the 8 mm specimens. To fully implement in-line IRT-based weld inspections more investigations are required

An Extended Review on Fabric Defects and Its Detection Techniques

In Textile Industry, Quality of the Fabric is the main important factor. At the initial stage, it is very essential to identify and avoid the fabrics faults/defects and hence human perception consumes lot of time and cost to reveal the fabrics faults. Now-a-days Automated Inspection Systems are very useful to decrease the fault prediction time and gives best visualizing clarity- based on computer vision and image processing techniques. This paper made an extended review about the quality parameters in the fiber-to-fabric process, fabrics defects detection terminologies applied on major three clusters of fabric defects knitting, woven and sewing fabric defects. And this paper also explains about the statistical performance measures which are used to analyze the defect detection process. Also, comparison among the methods proposed in the field of fabric defect detection

Ensemble Joint Sparse Low Rank Matrix Decomposition for Thermography Diagnosis System

Composite is widely used in the aircraft industry and it is essential for manufacturers to monitor its health and quality. The most commonly found defects of composite are debonds and delamination. Different inner defects with complex irregular shape is difficult to be diagnosed by using conventional thermal imaging methods. In this paper, an ensemble joint sparse low rank matrix decomposition (EJSLRMD) algorithm is proposed by applying the optical pulse thermography (OPT) diagnosis system. The proposed algorithm jointly models the low rank and sparse pattern by using concatenated feature space. In particular, the weak defects information can be separated from strong noise and the resolution contrast of the defects has significantly been improved. Ensemble iterative sparse modelling are conducted to further enhance the weak information as well as reducing the computational cost. In order to show the robustness and efficacy of the model, experiments are conducted to detect the inner debond on multiple carbon fiber reinforced polymer (CFRP) composites. A comparative analysis is presented with general OPT algorithms. Not withstand above, the proposed model has been evaluated on synthetic data and compared with other low rank and sparse matrix decomposition algorithms

Optimal morphological filter design for fabric defect detection

This paper investigates the problem of automated defect detection for textile fabrics and proposes a new optimal morphological filter design method for solving this problem. Gabor Wavelet Network (GWN) is adopted as a major technique to extract the texture features of textile fabrics. An optimal morphological filter can be constructed based on the texture features extracted. In view of this optimal filter, a new semi-supervised segmentation algorithm is then proposed. The performance of the scheme is evaluated by using a variety of homogeneous textile images with different types of common defects. The test results exhibit accurate defect detection with low false alarm, thus confirming the robustness and effectiveness of the proposed scheme. In addition, it can be shown that the algorithm proposed in this paper is suitable for on-line applications. Indeed, the proposed algorithm is a low cost PC based solution to the problem of defect detection for textile fabrics. © 2005 IEEE.published_or_final_versio

Restructured eigenfilter matching for novelty detection in random textures

A new eigenfilter-based novelty detection approach to find abnormalities in random textures is presented. The proposed algorithm reconstructs a given texture twice using a subset of its own eigenfilter bank and a subset of a reference (template) eigenfilter bank, and measures the reconstruction error as the level of novelty. We then present an improved reconstruction generated by structurally matched eigenfilters through rotation, negation, and mirroring. We apply the method to the detection of defects in textured ceramic tiles. The method is over 90 % accurate, and is fast and amenable to implementation on a production line.

Deep Learning based Defect classification and detection in SEM images: A Mask R-CNN approach

In this research work, we have demonstrated the application of Mask-RCNN (Regional Convolutional Neural Network), a deep-learning algorithm for computer vision and specifically object detection, to semiconductor defect inspection domain. Stochastic defect detection and classification during semiconductor manufacturing has grown to be a challenging task as we continuously shrink circuit pattern dimensions (e.g., for pitches less than 32 nm). Defect inspection and analysis by state-of-the-art optical and e-beam inspection tools is generally driven by some rule-based techniques, which in turn often causes to misclassification and thereby necessitating human expert intervention. In this work, we have revisited and extended our previous deep learning-based defect classification and detection method towards improved defect instance segmentation in SEM images with precise extent of defect as well as generating a mask for each defect category/instance. This also enables to extract and calibrate each segmented mask and quantify the pixels that make up each mask, which in turn enables us to count each categorical defect instances as well as to calculate the surface area in terms of pixels. We are aiming at detecting and segmenting different types of inter-class stochastic defect patterns such as bridge, break, and line collapse as well as to differentiate accurately between intra-class multi-categorical defect bridge scenarios (as thin/single/multi-line/horizontal/non-horizontal) for aggressive pitches as well as thin resists (High NA applications). Our proposed approach demonstrates its effectiveness both quantitatively and qualitatively.Comment: arXiv admin note: text overlap with arXiv:2206.1350

Contemporary Inspection and Monitoring for High-Speed Rail System

Non-destructive testing (NDT) techniques have been explored and extensively utilised to help maintaining safety operation and improving ride comfort of the rail system. As an ascension of NDT techniques, the structural health monitoring (SHM) brings a new era of real-time condition assessment of rail system without interrupting train service, which is significantly meaningful to high-speed rail (HSR). This chapter first gives a review of NDT techniques of wheels and rails, followed by the recent applications of SHM on HSR enabled by a combination of advanced sensing technologies using optical fibre, piezoelectric and other smart sensors for on-board and online monitoring of the railway system from vehicles to rail infrastructure. An introduction of research frontier and development direction of SHM on HSR is provided subsequently concerning both sensing accuracy and efficiency, through cutting-edge data-driven analytic studies embracing such as wireless sensing and compressive sensing, which answer for the big data’s call brought by the new age of this transport

Infrared Thermography for Weld Inspection: Feasibility and Application

Traditional ultrasonic testing (UT) techniques have been widely used to detect surface and sub-surface defects of welds. UT inspection is a contact method which burdens the manufacturer by storing hot specimens for inspection when the material is cool. Additionally, UT is only valid for 5 mm specimens or thicker and requires a highly skilled operator to perform the inspections and interpret the signals. Infrared thermography (IRT) has the potential to be implemented for weld inspections due to its non-contact nature. In this study, the feasibility of using IRT to overcome the limitations of UT inspection is investigated to detect inclusion, porosity, cracking, and lack of fusion in 38 weld specimens with thicknesses of 3, 8 and 13 mm. UT inspection was also performed to locate regions containing defects in the 8 mm and 13 mm specimens. Results showed that regions diagnosed with defects by the UT inspection lost heat faster than the sound weld. The IRT method was applied to six 3 mm specimens to detect their defects and successfully detected lack of fusion in one of them. All specimens were cut at the locations indicated by UT and IRT methods which proved the presence of a defect in 86% of the specimens. Despite the agreement with the UT inspection, the proposed IRT method had limited success in locating the defects in the 8 mm specimens. To fully implement in-line IRT-based weld inspections more investigations are required

ndt thermographic techniques on cfrp structural components for aeronautical application

Abstract This paper describes the application of active pulsed Thermography (PT) as a Non-Destructive Test (NDT) method for the investigation of CFRP aeronautical components. The analyzed specimens include T-shaped stringers, previously monitored by ultrasonic analysis, and laminated flat plates with internal production defects. Several set-up tests allowed to identify optimal configurations for the defect detection, according to specimen geometry and defect location. A custom post-processing algorithm has been developed to improve thermographic data for more precise defect characterization, whilst a successive full-field contrast mapping allows to achieve a reliable defect distribution map and a better definition on larger areas. Detection of defects was studied with a specific thermal contrast evaluation, with a suitable choice of undamaged reference area during the transient cooling phase. The influence of heating time and experimental set-up on the thermal contrast results has also been studied; moreover, the ability of thermographic technique to detect real small production defects with accuracy and reliability is verified for CFRP aeronautical components