• Proceedings of the KSR Conference
• /
• 2011.05a
• /
• pp.548-553
• /
• 2011

The lock-in thermography was employed to evaluate the defects in railway bogies. Prior to the actual application on railway bogies, in order to assess the detectability of known flaws, the calibration reference panel was prepared with various dimensions of artificial flaws. The panel was composed of polymer matrix composites, which were the same material with actual bogies. Through lock-in thermography evaluation, the optimal frequency of heat source was determined for the best flaw detection. Based on the defects information, the actual defect assessments on railway bogie were conducted with different types of railway bogies, which were used for the current operation. In summary, it was found that the novel infrared thermography technique could be an effective way for the inspection and the detection of surface defects on bogies since the infrared thermography method provided rapid and non-contact investigation of railway bogies.

• Steel and Composite Structures
• /
• v.33 no.3
• /
• pp.445-461
• /
• 2019

The ultrasonic guided wave-based technique has become one of the most promising methods in non-destructive evaluation and structural health monitoring, because of its advantages of large area inspection, evaluating inaccessible areas on the structure and high sensitivity to small damage. To further advance the development of damage detection technologies using ultrasonic guided waves for the inspection of welded components in structures, the transmission characteristics of the ultrasonic guided waves propagating through welded joints with various types of defects or damage in steel plates are studied and presented in this paper. A three-dimensional (3D) finite element (FE) model considering the different material properties of the mild steel, high strength steel and austenitic stainless steel plates and their corresponding welded joints as well as the interaction condition of the steel plate and welded joint, is developed. The FE model is validated against analytical solutions and experimental results reported in the literature and is demonstrated to be capable of providing a reliable prediction on the features of ultrasonic guided wave propagating through steel plates with welded joints and interacting with defects. Mode conversion and scattering analysis of guided waves transmitted through the different types of weld defects in steel plates are performed by using the validated FE model. Parametric studies are undertaken to elucidate the effects of several basic parameters for various types of weld defects on the transmission performance of guided waves. The findings of this research can provide a better understanding of the transmission behaviour of ultrasonic guided waves propagating through welded joints with defects. The method could be used for improving the performance of guided wave damage detection methods.

• 전자공학회논문지 IE
• /
• v.45 no.2
• /
• pp.20-27
• /
• 2008

In this paper, we propose a new image processing technique that reliably detects the various defects of TFT-LCD polarizing films. The image of polarizing film is acquisited from reflected laser beam First, we apply the morphological image processing technique to remove the background noise. Next, we use the 2-dimensional LMS adaptive filtering and statistical characteristics to detect the white and black defects. Performance of the proposed method is evaluated on real TFT-LCD polarizing film samples.

• Smart Structures and Systems
• /
• v.34 no.2
• /
• pp.129-143
• /
• 2024

In recent years, with the vigorous development of visual algorithms, a large amount of research has been conducted on blade surface defect detection methods represented by deep learning. Detection methods based on deep learning models must rely on a large and rich dataset. However, the geographical location and working environment of wind turbines makes it difficult to effectively capture images of blade surface defects, which inevitably hinders visual detection. In response to the challenge of collecting a dataset for surface defects that are difficult to obtain, a multi-class blade surface defect generation method based on the StyleGAN3 (Style Generative Adversarial Networks 3) deep learning model and PBGMs (Physics-Based Graphics Models) method has been proposed. Firstly, a small number of real blade surface defect datasets are trained using the adversarial neural network of the StyleGAN3 deep learning model to generate a large number of high-resolution blade surface defect images. Secondly, the generated images are processed through Matting and Resize operations to create defect foreground images. The blade background images produced using PBGM technology are randomly fused, resulting in a diverse and high-resolution blade surface defect dataset with multiple types of backgrounds. Finally, experimental validation has proven that the adoption of this method can generate images with defect characteristics and high resolution, achieving a proportion of over 98.5%. Additionally, utilizing the EISeg annotation method significantly reduces the annotation time to just 1/7 of the time required for traditional methods. These generated images and annotated data of blade surface defects provide robust support for the detection of blade surface defects.

• Journal of Welding and Joining
• /
• v.32 no.5
• /
• pp.50-57
• /
• 2014

Friction Stir Welding (FSW) is a complex process with several mutually interdependent parameters. A slight difference from known settings may lead to imperfections in the stirred zone. These inhomogeneities affect on the mechanical properties of the FSWed joints. In order to prevent the failure of the welded joint it is necessary to detect the most critical defects non-destructive. Especially critical defects are wormhole and lack of penetration (LOP), because of the difficulty of detection. Online thermography is used process-accompanying for defect detecting. A thermographic camera with a fixed position relating to the welding tool measures the heating-up and the cool down of the welding process. Lap joints with sound weld seam surfaces are manufactured and monitored. Different methods of evaluation of heat distribution and intensity profiles are introduced. It can be demonstrated, that it is possible to detect wormhole and lack of penetration as well as surface defects by analyzing the welding and the cooling process of friction stir welding by passive online thermography measurement. Effects of these defects on mechanical properties are shown by tensile testing.

• Proceedings of the KSR Conference
• /
• 2007.11a
• /
• pp.1940-1945
• /
• 2007

The wheelsets are one of most important component: damages in wheel tread and press fitted axle are a significant cost for railway industry. Since failure in railway wheelset can cause a disaster, regular inspection of defects in wheels and axles are mandatory. Ultrasonic testing, acoustic emission and eddy current testing method and so on regularly check railway wheelset in service. However, it is difficult to use this method because of its high viscosity and because its sensitivity is affected by temperature. Also, due to noise echoes it is difficult to detect defects initiation clearly with ultrasonic testing. It is necessary to develop a non-destructive technique that is superior to conventional NDT techniques in order to ensure the safety of railway wheelset. In the present paper, the new NDT technique is applied to the detection of surface defects for railway wheelset. To detect the defects for railway wheelset, the sensor for defect detection is optimized and the tests are carried out with respect to surface and internal defects each other. The results show that the surface crack depth of 1.5 mm in press fitted axle and internal crack in wheel could be detected by using the new method. The ICFPD method is useful to detect the defect that initiated in the tread of railway wheelset.

• Journal of Animal Science and Technology
• /
• v.64 no.5
• /
• pp.813-829
• /
• 2022

Internal and external defects of eggs should be detected to prevent cross-contamination of intact eggs by abnormal eggs during storage. Emerging detection technologies for abnormal eggs were introduced as an alternative to human inspection. The advanced technologies could rapidly detect abnormal eggs. Abnormal egg detection technologies using acoustic response, machine vision, and spectroscopy have been commercialized in the poultry industry. Non-destructive egg quality assessment methods meanwhile could preserve the value of eggs and improve detection efficiency. In order to improve detection efficiency, it is essential to select a proper algorithm for classifying the types of abnormal eggs. This review deals with the performance of the detection technologies for various types of abnormal eggs in recently published resources. In addition, the discriminant methods and detection algorithms of abnormal eggs reported in the published literature were investigated. Although the majority of the studies were conducted on a laboratory scale, the developed detection technologies for internal and external defects in eggs were technically feasible to obtain the excellent detection accuracy. To apply the developed detection technologies to the poultry industry, it is necessary to achieve the detection rates required from the industry.

• Journal of Korea Foundry Society
• /
• v.41 no.6
• /
• pp.521-527
• /
• 2021

This study presents a process management method for the detection of casting defects during in high-pressure die casting based on machine learning. The model predicts the defects of the next cycle by extracting the features appearing over the previous cycles. For design of the gearbox, the proposed model detects shrinkage defects with data from three cycles in advance with 98.9% accuracy and 96.8% recall rates.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.11 no.4
• /
• pp.99-104
• /
• 2011

In this paper the approach of neural network was proposed which detects a variety of defects in the molded parts. In an attempt to improve the response of the system, It is designed to minimize the use of memory via LookUp table in software. The goal of these methods was to extract the features of samples in learning of neural networks, overcoming the algorithms of defects detection and classification. Through the learning of 500 sample patterns of molded parts, defects of 3% molded parts was detected and classified as the incorrect diameter parts. We expect that proposed approach is an effective alternative to save test time and cost for defect detection of a fine pattern within the molded parts.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.21 no.1
• /
• pp.19-27
• /
• 2007

In this paper, we develope and implement a TFT-LCD cell defects detection algorithm using morphology. To detect the bright line or dark line defects and the bright pixel or dark pixel defects of the TFT-LCD cells, we determine the shape of the morphology operators considering the shape characteristics of the TFT-LCD sub pixels. Using dilation, erosion, and the subtraction operators, we extract gray level defects information. Then, we apply the optimal threshold method which shows the best results in terms of several criteria. Finally, we determine the defects using labelling method. From various experiments using TFT-LCD panels, the proposed algorithm shows superior results.