• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2049-2051
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• 2002

X-ray inspection of tires is now feasible and commercially available. The enabling technologies are economical yet powerful computing systems and high-resolution, high-dynamic range X-ray imaging systems customized for tire inspection. The key advantages of reduced inspection labor, highly consistent inspection and yield improvement are now realizable.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.11
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• pp.126-134
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• 1997

In this study, We made visual inspection system using Vision Board. It is consist of an illuminator (a fluorescent lamp), image input device (CCD (Charge-Coupled Device) camera), image processing system(Vision Board(FARAMVB-02)), image output device (video monitor, printer), and a measuring instrument(TELMN1000). Length measurement by visual inspection system make use of 100mm guage block(instead of calculating distance between a camera and a object). It measured horizontal and vertical length factor from 400mm to 650mm by increasing 50mm. In this place, measured horizontal and vertical length factor made use of length measure- ment of a injection. A measuring instrument used to ompare a measured length of a injection visual inspection system with it. In conclusion, length measurement of a injection compared a measuring instrument with visual inspecion system using length factor of 100mm gauge block. We find that maximum error of length is 0.55mm when it compar with the measuring value of two devices(FARAMVB-02, TELMN1000). Program of visual inspection system is made up Borland C++3.1.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.6
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• pp.1155-1160
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• 2017

This paper describes a system implementation for the automatic inspection on the ventilating electronic device based on the reliability improvement. To be enhancement, the inspection error is minimized by the automatic inspection system on the ventilating apparatuses against the manual inspecting system. The system consists of the control system, software structure and monitoring system to be scanning the inspection processing. The inspection system for reliability improvement is evaluated in Gage Repeatability and Reproducibility. The experimental results are improved about 2 times inspecting speed, measured error ${\pm}0.02V$, effectiveness of discriminating performance 15%, missing probability 17% and false alarm probability 12% respectively in comparing with the manual inspection based on the wind pressure sensor. The system will be also improved more by making database and product bar codes for the total quality control system to the effective reliability enhancement in the future.

• Journal of Korean Society for Quality Management
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• v.6 no.1
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• pp.3-13
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• 1978

It is worthwhile to know how the quality inspection is practised to assure the product quality for the export articles, and what problem is resulted from the current quality inspection system for the export articles. The aim of this paper is to survey the status of quality inspection system for the export articles and to design the quality inspection system for the export articles so as to not only solve the problems arising from the current system but also improve the product quality for the export articles. To find out the facts the survey is conducted by sending a prepared questionnaire both to the export industries and to the export quality inspection agencies authorized by the Ministry of Commerce and Industry. The export industries surveyed are selected to provide broad representation with respect to the type of industry as well as to the size of company. The survey shows that about 50 percent of the responding industries desire to discontinue the current quality inspection system for the export articles, and other 50 percent of them desire to continue it until all the export industries can make the best use of quality control techniques.

• Journal of IKEEE
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• v.28 no.3
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• pp.474-478
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• 2024

In this paper, we proposes a deep learning network for quality inspection in a multi-camera inline inspection system for pharmaceutical containers. The proposed deep learning network is specifically designed for pharmaceutical containers by using data produced in real manufacturing environments, leading to more accurate quality inspection. Additionally, the use of an inline-capable deep learning network allows for an increase in inspection speed. The development of the deep learning network for quality inspection in the multi-camera inline inspection system consists of three steps. First, a dataset of approximately 10,000 images is constructed from the production site using one line camera for foreign substance inspection and three area cameras for dimensional inspection. Second, the pharmaceutical container data is preprocessed by designating regions of interest (ROI) in areas where defects are likely to occur, tailored for foreign substance and dimensional inspections. Third, the preprocessed data is used to train the deep learning network. The network improves inference speed by reducing the number of channels and eliminating the use of linear layers, while accuracy is enhanced by applying PReLU and residual learning. This results in the creation of four deep learning modules tailored to the dataset built from the four cameras. The performance of the proposed deep learning network for quality inspection in the multi-camera inline inspection system for pharmaceutical containers was evaluated through experiments conducted by a certified testing agency. The results show that the deep learning modules achieved a classification accuracy of 99.4%, exceeding the world-class level of 95%, and an average classification speed of 0.947 seconds, which is superior to the world-class level of 1 second. Therefore, the effectiveness of the proposed deep learning network for quality inspection in a multi-camera inline inspection system for pharmaceutical containers has been demonstrated.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.1120-1123
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• 1995

In this paper, an automatic B/STUD inspection system has been developed using the computer aided vision system. Index Table has been used to get the rapid measurement and multi-camera has been used to get the high resolution in mechanical system. Camera calibration was suggested to perform the reliable Inspection. Image processing and data analysis algorithms for B/STUD inspection system has been investigated and were performed quickly with high accuracy. As a result, Inspection system of a B/STUD can be measured with a high resolution in real time.

• Journal of the Korean Society for Precision Engineering
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• v.22 no.1
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• pp.82-88
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• 2005

The damage inspection of container surface is performed by the expert inspectors at the container terminal gate of harbor. In this paper, we substitute the expert's capability with the damage inspection system using the artificial intelligent control algorithm and vision system, so we can improve the work environment and effectively decrease the inspection time and cost. Firstly, using six CCD cameras attached to the terminal gate, whole container is partially captured according to eleven sensors aligned with the entering direction of container. Captured partial images are inspected by the fuzzy system which the expert's technology is embedded. Next, we compose partial images to be a complete container image through the correlation coefficient method. Complete container image is saved to solve future troublesome problems. In this paper, the effectiveness of the proposed system was verified through the field test.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.04a
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• pp.281-285
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• 1996

In this paper, I made visual inspection system using Vision Board and it is consist of an illuminator (a fluorescent lamp), image input device(CCD(Charge)Coupled Device) camera), image processing system(Vision Board(FARAMVB-02), image output device(videomonitor, printer), a measuring instrument(TELMN1000). Length measurement by visual inspection system is used 100mm gauge block instead of calculating distance between camera and object, it measured horizontal and vertical length factor from 400mm to 650mm by increasing 50mm. In this place, measured horizontal and vertical length factor made use of length measurement of a injection. A measuring instrument used to compare a measured length of a injection visual inspection system with it. In conclusion, length measurement of a injection compared a measuring instrument withvisual inspecion system using length factor of 100mm guage block. Maximum error of length compared two devices a measuring instrument with visual inspection system is 0.55mm. And operation program is made up Borland C++ 3.1. By changing, it is applied to various uses.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.1
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• pp.75-80
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• 2008

This paper presents a vision based autonomous inspection system for welding quality control of a RV sinking seat. In order to overcome the precision error that arises from a visible inspection by an operator in the manufacturing process of a RV sinking seat, the machine vision based welding quality control system is proposed. It consists of the CMOS camera and the NI vision system. The geometry of the welding bead, which is the welding quality criteria, is measured by using the captured image with a median filter applied on it. The image processing software for the system was developed using the NI LabVIEW software. The proposed welding quality inspection system for RV sinking seat was verified using experimentation.

• International Journal of Precision Engineering and Manufacturing
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• v.5 no.1
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• pp.60-68
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• 2004

The purpose of this paper is to establish an effective inspection system by using OMM (On-Machine Measurement) system based PC-NC. This system can reduce manufacturing lead time because a workpiece is inspected at every machining process and the manufacturing system which includes inspection faculty is able to realize on-line process on CNC machining center. The proposed OMM system is composed of a few algorithms for determination of inspection parameters. It is accomplished by determining the number of measuring points, their location, measuring path using fuzzy logic, Hammersley's method, TSP (Traveling Salesperson Problem) algorithm. The inspection feature applied to this system is based on machining feature. This method is tested by simulation and experiment that are analyzed measuring data and geometry tolerance.