• Proceedings of the KSME Conference
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• 2005.11a
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• pp.61.2-61.2
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• 2005

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.2
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• pp.7-13
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• 2021

Machine vision is a technology that helps the computer as if a person recognizes and determines things. In recent years, as advanced technologies such as optical systems, artificial intelligence and big data advanced in conventional machine vision system became more accurate quality inspection and it increases the manufacturing efficiency. In machine vision systems using deep learning, the image quality of the input image is very important. However, most images obtained in the industrial field for quality inspection typically contain noise. This noise is a major factor in the performance of the machine vision system. Therefore, in order to improve the performance of the machine vision system, it is necessary to eliminate the noise of the image. There are lots of research being done to remove noise from the image. In this paper, we propose an autoencoder based machine vision system to eliminate noise in the image. Through experiment proposed model showed better performance compared to the basic autoencoder model in denoising and image reconstruction capability for MNIST and fashion MNIST data sets.

• Journal of the Semiconductor & Display Technology
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• v.20 no.2
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• pp.14-18
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• 2021

Electric Vehicle industry requires high technologies to stabilize apparatuses for the Coolant heater manufacturing. Vibrations of Leak Machine are one of the most critical factors for causing delivered of the defective product or poor inspection, which are the main reasons of the defects. In this study, the structure of the Leak Machine was analyzed through the experiment and the computer simulation to investigate the main reasons of the vibrations, and further to alter the design for the improved stability. And that design alterations were applied to the machine to identify the effects of those alterations. The result of the study shows that design alterations of the Leak Machine can effectively suppress about 97.8% of the vibrations, and further can improving the Inspection precision of the Coolant heater.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.11
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• pp.1089-1095
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• 2010

We propose an image segmentation method for auto-teaching system of PCB (Printed Circuit Board) assembly inspection machines. The inspection machine acquires images of all components in PCB, and then compares each image with its standard image to find the assembly errors such as misalignment, inverse polarity, and tombstone. The component window that is the area of component to be acquired by camera, is one of the teaching data for operating the inspection machines. To reduce the teaching time of the machine, we newly develop the image processing method to extract the component window automatically from the image of PCB. The proposed method segments the component window by excluding the soldering parts as well as board background. We binarize the input image by use of HSI color model because it is difficult to discriminate the RGB colors between components and backgrounds. The linear combination of the binarized images then enhances the component window from the background. By use of the horizontal and vertical projection of histogram, we finally obtain the component widow. The experimental results are presented to verify the usefulness of the proposed method.

• Journal of Practical Engineering Education
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• v.6 no.1
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• pp.37-42
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• 2014

The automated visual inspection system (machine vision system) for quality assurance is important factory automation equipment in the manufacturing industries, such as display, semiconductor, etc. There is a lot of demand for the machine vision engineers. However, there are no technical training courses for machine vision technologies in vocational schools, colleges and universities. In this paper, we present the implementation of line scan camera based equipment for technical training of the automated visual inspection system. The training system consists of the X-Y stage which is widely used in machine vision industries and its variable image resolution are set to $10-30{\mu}m$. Additionally, this training system can attach the industrial illumination, either the direct illuminator or coaxial illuminator, for verifying the effect of illuminations. This means that the trainee can have a practical training in various equipment conditions and the training system is similar to the automated visual inspection system in industries.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.6
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• pp.2624-2630
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• 2013

In LED chip packaging, die bonding is a very important process which fixes the LED chip on the lead flame to provide enough strength for the next process. During the process, inspection processes are very important to detect exact locations of dispensed epoxy dots and to determine bonding status of dies whether they are lies at exact positions with sufficient bonding strength. In this study, a useful machine vision based inspection system is proposed for the LED die bonder. In the proposed system, 2 cameras are used for epoxy dot position detection and 2 cameras are sued for die attaching status determination. New vision processing algorithm is proposed, and its efficiency is verified through required field experiments. Measured position error is less than $X:-29{\mu}m$, $Y:-32{\mu}m$ and rotation error:$3^{\circ}$ using proposed vision algorithm. It is concluded that the proposed machine vision based inspection system can be successfully implemented on the developed die bonding system.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.1235-1238
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• 2003

A rule-based CAPP(computer aided process planning) system is proposed in this research to develop integrated manufacturing process of machining and inspection using OMM(On-Machine Measurement) device. Generally workpiece composed of many primitive form features. This features are determined optimum inspection sequence by analyzing the feature information such as features-relationship, probe approach direction and etc. Proposed paper is more efficient method of CAIP(computer aided inspection planning) considered machining process

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.12
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• pp.1109-1114
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• 2015

This paper presents an improved learning-based visual inspection method for auto parts inspection in severe lighting changes. Automobile sunroof frames are produced automatically by robots in most production lines. In the sunroof frame manufacturing process, there is a quality problem with some parts such as volts are missed. Instead of manual sampling inspection using some mechanical jig instruments, a learning-based machine vision system was proposed in the previous research[1]. But, in applying the actual sunroof frame production process, the inspection accuracy of the proposed vision system is much lowered because of severe illumination changes. In order to overcome this capricious environment, some selective feature vectors and cascade classifiers are used for each auto parts. And we are able to improve the inspection accuracy through the re-learning concept for the misclassified data. The effectiveness of the proposed visual inspection method is verified through sufficient experiments in a real sunroof production line.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.11
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• pp.84-92
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• 1997

The purpose of this study is to develop a geometric error model and path compensation algorithm for rotating axes of the 5-axis machine tools, by a method to calibrate a rotary table using one master ball and three LVDTs. It was developed a new methodology to measure 3 translation errors of the rotary table and with a compensation procedure for setup errors of the master ball. The method is experimentally verified using a ball-table and on-machine inspection method. The results showed that the geometric error models with the path compensation strategy can be practically used as a means for improving the accuracy of the machine tools with rotary table.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.34 no.5
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• pp.371-381
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• 2021

A machine vision inspection system consists of a camera, optics, illumination, and image acquisition system. Especially a scanning system has to be made to measure a large inspection area. Therefore, a machine vision line scan camera needs a line scan light source. A line scan light source should have a high light intensity and a uniform intensity distribution. In this paper, an offset calibration and slope calibration methods are introduced to obtain a uniform light intensity profile. Offset calibration method is to remove the deviation of light intensity among channels through adding intensity difference. Slope calibration is to remove variation of light intensity slope according to the control step among channels through multiplying slope difference. We can obtain an improved light intensity profile through applying offset and slope calibration simultaneously. The proposed method can help to obtain clearer image with a high precision in a machine vision inspection system.