• International Journal of Control, Automation, and Systems
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• v.4 no.1
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• pp.96-104
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• 2006

We propose a path planning method to improve the productivity of AOI (automated optical inspection) machines in PCB (printed circuit board) assembly lines. The path-planning problem is the optimization problem of finding inspection clusters and the visiting sequence of cameras to minimize the overall working time. A unified method is newly proposed to determine the inspection clusters and visiting sequence simultaneously. We apply a hybrid genetic algorithm to solve the highly complicated optimization problem. Comparative simulation results are presented to verify the usefulness of the proposed method.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.109.2-109
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• 2001

In the PCB assembly lines, as components become more complex and smaller, the conventional inspection method using traditional ICT and function test show their limitations in application. The automatic optical inspection(AOI) gradually becomes the alternative in the PCB assembly line. In Particular, the PCB inspection machines need more reliable and flexible object recognition algorithms for high inspection accuracy. The conventional AOI machines use the algorithmic approaches such as template matching, Fourier analysis, edge analysis, geometric feature recognition or optical character recognition (OCR), which mostly require much of teaching time and expertise of human operators. To solve this problem, in this paper, a statistical learning based part recognition method is proposed. The performance of the ...

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.288-288
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• 2000

Reactor vessel is one of the most important equipment with regard to the safety of nuclear power plant. Thus nuclear regulation requires its periodical examination by certified inspection experts. Conventional reactor inspection machines are obsolete, hard to handle, and very expensive. To solve these problems we developed robotic reactor vessel inspection system which are small, easy to use for inspection, cost effective, and convenient in operation. This paper describes the main features of Main Control System which is one part of robotic inspection equipment we developed.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.434-437
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• 2003

Evaluation or the patterning accuracy of e-beam lithography machines requires a high precision inspection system that is capable of measuring the true xy-locations of fiducial marks generated by the e-beam machine under test. Fiducial marks are fabricated on a single photo mask over the entire working area in the form of equally spaced two-dimensional grids. In performing the evaluation, the principles of self-calibration enable to determine the deviations of fiducial marks from their nominal xy-locations precisely, not being affected by the motion errors of the inspection system itself. It is. however, the fact that only repeatable motion errors can be eliminated, while random motion errors encountered in probing the locations of fiducial marks are not removed. Even worse, a random error occurring from the measurement of a single mark propagates and affects in determining locations of other marks, which phenomenon in fact limits the ultimate calibration accuracy of e-beam machines. In this paper, we describe an uncertainty analysis that has been made to investigate how random errors affect the final result of self-calibration of e-beam machines when one uses an optical inspection system equipped with high-resolution microscope objectives and a precision xy-stages. The guide of uncertainty analysis recommended by the International Organization for Standardization is faithfully followed along with necessary sensitivity analysis. The uncertainty analysis reveals that among the dominant components of the patterning accuracy of e-beam lithography, the rotationally symmetrical component is most significantly affected by random errors, whose propagation becomes more severe in a cascading manner as the number of fiducial marks increases

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.350-353
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• 1996

In this paper, based on the performance analysis of serial production lines with quality inspection machines, we develope an buffer size optimization method to maximize the production rate. The total sum of buffer sizes are given and a constant, and under this constraint, using the linear approximation method, we suggest a closed form solution for the optimization problem with an acceptable error. Also, we show that the upstream and downstream buffers of the worst performance machine have a significant effect on the production rate. Finally, the suggested methods are validated by simulations.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.10
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• pp.1508-1515
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• 2017

This paper proposes the defect detection method for automatic inspection machines in printed circuit boards (PCBs) manufacturing system. The defects of PCB such as open, short, pin hole and scratch can be detected by comparing the standard image and the target image. The standard image is obtained from CAD file such as ODB++ format, and the target image is obtained by arranging, filtering and binarization of captured PCB image. Since the PCB size is too large and image resolution is too high, the image processing requires a lot of memory and computational time. The wavelet transform is applied to compress the standard and target images, which results in reducing the memory and computational time. To increase the inspection accuracy, we utilize the he HH-domain as well as LL-domain of the transformed images. Experimental results are finally presented to show the performance improvement of the proposed method.

• Journal of Computational Design and Engineering
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• v.3 no.2
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• pp.91-101
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• 2016

The automated part quality inspection poses many challenges to the engineers, especially when the part features to be inspected become complicated. A large quantity of part inspection at a faster rate should be relied upon computerized, automated inspection methods, which requires advanced quality control approaches. In this context, this work uses innovative methods in remote part tracking and quality control with the aid of the modern equipment and application of support vector machine (SVM) learning approach to predict the outcome of the quality control process. The classifier equations are built on the data obtained from the experiments and analyzed with different kernel functions. From the analysis, detailed outcome is presented for six different cases. The results indicate the robustness of support vector classification for the experimental data with two output classes.

• Transactions of the Korean Society of Automotive Engineers
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• v.25 no.3
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• pp.273-286
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• 2017

We develop an inspection method for bearing faults with a rapid change in the rotation speed and present indexes for the pass/fail inspection. At the end of line, impulse noises generated by the operation of machines and conveyors may distort the inspection results. In this paper, we present robust inspection indexes for bearing faults under impulse noises, by taking into account fault signals having pulse train. Using logistic regression, we optimize the pass/fail criterion for each index and evaluate the performance of the inspection indexes based on the total error rate.

• Proceedings of the Korea Contents Association Conference
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• 2009.05a
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• pp.1141-1149
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• 2009

In Bone Mineral Density(BMD) measurements, accuracy and precision must be superior in order to know the small changes in bone mineral density and actual biological changes. Therefore the purpose of this study is to increase the reliability of bone mineral density inspection through appropriate management of image quality from machines and inspectors. For the machine management method, the recommended phantom from each bone mineral density machine manufacturer was used to take 10~25 measurements to determine the standard amount and permitted limit. On each inspection day, measurements were taken everyday or at least three times per week to verify the whether or not change existed in the amount of actual bone mineral density. Also evaluations following Shewhart control chart and CUSUM control chart rules were made for the bone mineral density figures from the phantoms used for measurements. Various forms of management became necessary for machine installation and movement. For the management methods of inspectors, evaluation of the measurement precision was conducted by testing the reproducibility of the exact same figures without any real biological changes occurring during reinspection. There were two measurement methods followed: patients were either measured twice with 30 measurements or three times with 15 measurements. An important point to make regarding measurements is that after the first inspection and any other inspection following, the patient was required to come off the inspection table completely and then get back on for any further measurements. With a 95% confidence level, the precision error produced from the measurement bone mineral figures produced a precision error of 2.77 times the minimum of the biological bone mineral density change (Least significant change: LSC). In order to assure reliability in inspection, there needs to be good oversight of machine management and measurer for machine operation and inspection error. Accuracy error in machines needs to be reduced to under 1% for scientific development in bone mineral density machines.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.4
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• pp.181-189
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• 2023

In this paper, we present a case study of developing MVIS (Machine Vision Inspection System) designed for exterior quality inspection of stamping dies used in the production of automotive exterior components in a small to medium-sized factory. While the primary processes within the factory, including machining, transportation, and loading, have been automated using PLCs, CNC machines, and robots, the final quality inspection process still relies on manual labor. We implement the MVIS with general-purpose industrial cameras and Python-based open-source libraries and frameworks for rapid and low-cost development. The MVIS can play a major role on improving throughput and lead time of stamping dies. Furthermore, the processed inspection images can be leveraged for future process monitoring and improvement by applying deep learning techniques.