Application Of Defect Detection In Gluing Line Using Shape-Based Matching Approach

This paper investigates various approaches for automated inspection of gluing process using shape-based matching application. A new supervised defect detection approach to detect gap defect, bumper defect and bubble defect in gluing application is proposed. The creation of region of interest for important region of the object is further explained. The Correlation algorithm to determine better image processing result using template matching techniques is also proposed. This technique does not only reduce execution time, but also produce high accuracy in defect detection rate. The recognition efficiency will achieve more than 95% with defect’s data for further process

Similarity Measures for Automatic Defect Detection on Patterned Textures

Similarity measures are widely used in various applications such as information retrieval, image and object recognition, text retrieval, and web data search. In this paper, we propose similarity-based methods for defect detection on patterned textures using five different similarity measures, viz., Normalized Histogram Intersection Coefficient, Bhattacharyya Coefficient, Pearson Product-moment Correlation Coefficient, Jaccard Coefficient and Cosine-angle Coefficient. Periodic blocks are extracted from each input defective image and similarity matrix is obtained based on the similarity coefficient of histogram of each periodic block with respect to itself and other all periodic blocks. Each similarity matrix is transformed into dissimilarity matrix containing true-distance metrics and Ward’s hierarchical clustering is performed to discern between defective and defect-free blocks. Performance of the proposed method is evaluated for each similarity measure based on precision, recall and accuracy for various real fabric images with defects such as broken end, hole, thin bar, thick bar, netting multiple, knot, and missing pick

2-D iteratively reweighted least squares lattice algorithm and its application to defect detection in textured images

In this paper, a 2-D iteratively reweighted least squares lattice algorithm, which is robust to the outliers, is introduced and is applied to defect detection problem in textured images. First, the philosophy of using different optimization functions that results in weighted least squares solution in the theory of 1-D robust regression is extended to 2-D. Then a new algorithm is derived which combines 2-D robust regression concepts with the 2-D recursive least squares lattice algorithm. With this approach, whatever the probability distribution of the prediction error may be, small weights are assigned to the outliers so that the least squares algorithm will be less sensitive to the outliers. Implementation of the proposed iteratively reweighted least squares lattice algorithm to the problem of defect detection in textured images is then considered. The performance evaluation, in terms of defect detection rate, demonstrates the importance of the proposed algorithm in reducing the effect of the outliers that generally correspond to false alarms in classification of textures as defective or nondefective

In-Situ Defect Detection in Laser Powder Bed Fusion by Using Thermography and Optical Tomography—Comparison to Computed Tomography

Among additive manufacturing (AM) technologies, the laser powder bed fusion (L-PBF) is one of the most important technologies to produce metallic components. The layer-wise build-up of components and the complex process conditions increase the probability of the occurrence of defects. However, due to the iterative nature of its manufacturing process and in contrast to conventional manufacturing technologies such as casting, L-PBF offers unique opportunities for in-situ monitoring. In this study, two cameras were successfully tested simultaneously as a machine manufacturer independent process monitoring setup: a high-frequency infrared camera and a camera for long time exposure, working in the visible and infrared spectrum and equipped with a near infrared filter. An AISI 316L stainless steel specimen with integrated artificial defects has been monitored during the build. The acquired camera data was compared to data obtained by computed tomography. A promising and easy to use examination method for data analysis was developed and correlations between measured signals and defects were identified. Moreover, sources of possible data misinterpretation were specified. Lastly, attempts for automatic data analysis by data integration are presented

Correlation of micro and nano–scale defects with WVTR for aluminium oxide barrier coatings for flexible photovoltaic modules

This paper seeks to establish a correlation between surface topographical defects and water vapour transmission rate (WVTR) measured under laboratory conditions for aluminium–oxide (Al2O3) barrier film employed in flexible photovoltaic (PV) modules. Defects in the barrier layers of PV modules causing high WVTR are not well characterised and understood. A WVTR of ~10−1 g/m2/day is sufficient for the most packaging applications, but ≤10−6 g/m2/day is required for the encapsulation of long–life flexible PV modules (Carcia et al., 2010a, 2010b). In this study, surface metrology techniques along with scanning electron microscopy (SEM) were used for a quantitative characterisation of the barrier film defects. The investigation have provided clear evidence for the correlation of surface defect density and the transmission of water vapour through the barrier coating layer. The outcomes would appear to suggest that small numbers of large defects are the dominant factor in determining WVTR for these barrier layers

A match coefficient approach for damage imaging in structural components by ultrasonic synthetic aperture focus

Ultrasonic Synthetic Aperture Focus (SAF) techniques are commonly used to image structural defects. In this paper, a variation of SAF based on ideas borrowed from Matched Field Processing (MFP) is evaluated to reduce artifacts and sidelobes of the resulting images. In particular, instead of considering the full RF ultrasonic waveforms for the SAF time backpropagation, only selected features from the waveforms are utilized to form a “data vector” and a “replica” (expected) vector of MFP. These vectors are adaptive for the pair of transmitter-receiver and the focus point. The image is created as a matched filter between these two vectors. Experimental results are shown for an isotropic and homogenous metallic plate with simulated defects, probed by six piezoelectric patches used as receivers or transmitters

Automatic detection of welding defects using the convolutional neural network

Quality control of welded joints is an important step before commissioning of various types of metal structures. The main obstacles to the commissioning of such facilities are the areas where the welded joint deviates from acceptable defective standards. The defects of welded joints include non-welded, foreign inclusions, cracks, pores, etc. The article describes an approach to the detection of the main types of defects of welded joints using a combination of convolutional neural networks and support vector machine methods. Convolutional neural networks are used for primary classification. The support vector machine is used to accurately define defect boundaries. As a preprocessing in our work, we use the methods of morphological filtration. A series of experiments confirms the high efficiency of the proposed method in comparison with pure CNN method for detecting defects

Integrated process of images and acceleration measurements for damage detection

The use of mobile robots and UAV to catch unthinkable images together with on-site global automated acceleration measurements easy achievable by wireless sensors, able of remote data transfer, have strongly enhanced the capability of defect and damage evaluation in bridges. A sequential procedure is, here, proposed for damage monitoring and bridge condition assessment based on both: digital image processing for survey and defect evaluation and structural identification based on acceleration measurements. A steel bridge has been simultaneously inspected by UAV to acquire images using visible light, or infrared radiation, and monitored through a wireless sensor network (WSN) measuring structural vibrations. First, image processing has been used to construct a geometrical model and to quantify corrosion extension. Then, the consistent structural model has been updated based on the modal quantities identified using the acceleration measurements acquired by the deployed WSN. © 2017 The Authors. Published by Elsevier Ltd