• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.20 no.5
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• pp.666-672
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• 2011

In this paper, we describes the development of automatic inspection system for detecting the defects on photovoltaic wafer by using machine vision. Until now, The defect inspection process was manually performed by operators. So these processes caused the produce of poorly-made articles and inaccuracy results. To improve the inspection accuracy, the inspection system is not only configured, but the image processing algorithm is also developed. The inspection system includes dimensional verification and pattern matching which compares a 2-D image of an object to a pattern image the method proves to be computationally efficient and accurate for real time application and we confirmed the applicability of the proposed method though the experience in a complex environment.

• Proceedings of the KSR Conference
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• 2007.11a
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• pp.1940-1945
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• 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 Korean Society of Industrial and Systems Engineering
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• v.46 no.4
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• pp.312-320
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• 2023

Recently, there has been an increasing attempt to replace defect detection inspections in the manufacturing industry using deep learning techniques. However, obtaining substantial high-quality labeled data to enhance the performance of deep learning models entails economic and temporal constraints. As a solution for this problem, semi-supervised learning, using a limited amount of labeled data, has been gaining traction. This study assesses the effectiveness of semi-supervised learning in the defect detection process of manufacturing using the MixMatch algorithm. The MixMatch algorithm incorporates three dominant paradigms in the semi-supervised field: Consistency regularization, Entropy minimization, and Generic regularization. The performance of semi-supervised learning based on the MixMatch algorithm was compared with that of supervised learning using defect image data from the metal casting process. For the experiments, the ratio of labeled data was adjusted to 5%, 10%, 25%, and 50% of the total data. At a labeled data ratio of 5%, semi-supervised learning achieved a classification accuracy of 90.19%, outperforming supervised learning by approximately 22%p. At a 10% ratio, it surpassed supervised learning by around 8%p, achieving a 92.89% accuracy. These results demonstrate that semi-supervised learning can achieve significant outcomes even with a very limited amount of labeled data, suggesting its invaluable application in real-world research and industrial settings where labeled data is limited.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.6 no.4
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• pp.46-53
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• 1997

This paper presents a method of detection localized defects on tapered roller bearing in main spindle of machine tool system. The feature vectors, i.e. statistical parameters, in time-domain analysis technique have been calculated to extract useful features from acoustic emission signals. These feature vectors are used as the input feature of an neural network to classify and detect bearing defects. As a results, the detection of bearing defect conditions could be sucessfully performed by using an neural network with statistical parameters of acoustic emission signals.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.3
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• pp.256-261
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• 2013

In the steel manufacturing area, researches for defect inspection receive a big attention for quality control. This paper proposes an algorithm to detect a scratch defect on steel billets. This algorithm takes ROIs (Regions of Interest), and extracts 11 features which represent properties of defect on a ROI. SVM (Support Vector Machine) is used to classify defect and normal ROIs. The algorithm classifies a frame image of a Billet as a defect image if there is one or more defect ROIs. In the experiments, the proposed algorithm had reliable classifying accuracy.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2000.10a
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• pp.665-668
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• 2000

This paper presents development of a different substance monitoring system base reliable detection between the conveyer belt and hopper used lot materials transport line of steel company. Conventional detection method of a piece of iron separation system is losed the confidence, because of the place with bad surroundings of measurement so much that materials Production line are completely exposed to dust, moisture and vibration. For the solution of this problem, we developed a different substance detection system using the acoustic emittion sensor and one chip microprocessor which is available for bad surroundings and inexpensive. The reliability of the system was estimated by experiment.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.8
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• pp.49-56
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• 1997

In recent years, ultrasonic technics has been widely applied to industrial fields and its application range has been expanded as a result of continuous research and development. This paper is concerned with development of post-processor program for ultrasonic test and two-axis automatic ultrasonic system for application of industrial fields. Test results of ultrasonic test post-processor program and two-axis auto- matic ultrasonic system have a good agreement with results of ultrasonic evaluation for defect detection in industrial fields. Therefore we think that the developed ultrasonic test post-processor program and two- axis automatic ultrasonic system in this work is very useful for application of industrial fields.

• Structural Engineering and Mechanics
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• v.9 no.2
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• pp.181-193
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• 2000

The objective of this research is to propose a new global damage detection parameter, termed as the static defect energy (SDE). This candidate parameter possesses the ability to detect, locate and quantify structural damage. To have a full understanding about this parameter and its applications, the scope of work can be divided into several tasks: theory and formulation, numerical simulation studies, experimental verification and feasibility studies. This paper only deals with the first part of the task. Brief introduction will be given to the dynamic defect energy (DDE) after systematically reviewing the previous works. Process of applying the perturbation method to the oscillatory system to obtain a static expression will be followed. Two implementation methods can be used to obtain SDE equations and the diagrams. Both results are equally good for damage detection.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.4 no.4
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• pp.803-808
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• 2000

This paper presents development of a different substance monitoring system base reliable detection between the conveyer belt and hopper used for materials transport line of steel company. Conventional detection method of a piece of iron separation system is losed the confidence, because of the place with bad surroundings of measurement so much that materials production line are completely exposed to dust, moisture and vibration. For the solution of this problem, we developed a different substance detection system using the acoustic emittion sensor and one chip microprocessor which is available for bad surroundings and inexpensive. The reliability of the system was estimated by experiment.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1240-1242
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• 1996

In a still mill, the effective surface defect inspection algorithm is necessary. For this purpose, this paper proposed the preprocessing algorithm for surface defect inspection of cold mill strip. This consists of live steps. They are edge detection, binarizing, noise deletion, combining of fragmented defect and selecting the largest defect. Especially, binarizing is a critical problem. Bemuse the performance of the preprocessing is largely depend on the binarized image. So, we develope the adaptive thresholding method, which is multilevel thresholding. The thresholding value is varied according to the mean graylevel value of each test image. To investigate the performance of the proposed algorithm, we classified the detected defect using neural network. The test image is 20 defect images captured at German Sick Co. This algorithm is proved to have good property in cold mill strip surface inspection.