• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2007.11a
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• pp.983-986
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• 2007

Concrete surface grinding is required for flatness and adhesiveness of concrete surface. The procedure is, however, labor intensive and has a hazardous work condition. Also, the productivity and the quality of concrete surface grinding depend on the levels of worker. Thus, the development of remote controlled concrete surface grinding equipment is necessary to prevent the environmental pollution and to protect the workers from hazardous work condition. However, it is difficult to evaluate the grinded surface objectively in a remote controlled system. The machine vision system developed in this study takes the images of grinded surface with the network camera for image processing. Then, by representing the quality test results to the graphic MMI program of the remote control station, the quality control system is constructed. The machine vision algorithm means the image processing algorithm of grinded concrete surface and this paper presents the objective quality control standard of grinded concrete surface through the application of the suggested algorithm.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.07a
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• pp.245-246
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• 2020

본 논문에서는 블록형 코딩 방식을 통해 비전공자가 스스로 머신러닝의 쉽게 원리를 구현해 볼 수 있는 딥아이( D.I.Y, Deep AI Yourself) 플랫폼을 제안하였다. 딥아이는 구글의 오픈 소스 블록형 코딩 툴 개발 라이브러리인 Blockly를 기반으로 머신러닝 알고리즘을 쉽게 구현할 수 다양한 블록으로 구성되어 있다. Blockly는 CSR 기반이며 사용자가 개발한 블록 코드는 내부적으로 코드 생성기에 의해 파이썬 코드 등으로 변환되어 백엔드 서버에서 처리를 하며 결과를 사용자에게 제공한다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.5
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• pp.1558-1563
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• 2010

This paper describes a study on algorithm for the development of machine vision system as well as the inspection of automobile's plastic part locking lever to replace a human worker's eye inspection. Before developing the machine vision system based on the PC, the purpose of this research is to develop the algorithm to decide whether a product is a good/bad one in real time inspection. NI-LabVIEW software is used in the inspection method and an inspection program is developed using LabVIEW Vision image functions. The inspection program was built and validated to help the system operator set up the inspection area and change the criteria number in the program.

• Smart Media Journal
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• v.11 no.9
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• pp.47-55
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• 2022

Quality control in the smart factory manufacturing process is an important factor. Currently, quality inspection of home appliance manufacturing parts produced by the mold process is mostly performed with the naked eye of the operator, resulting in a high error rate of inspection. In order to improve the quality competition, an automatic defect detection system was designed and implemented. The proposed system acquires an image by photographing an object with a high-performance scan camera at a specific location, and reads defective products due to scratches, dents, and foreign substances according to the vision inspection algorithm. In this study, the depth-based branch decision algorithm (DBD) was developed to increase the recognition rate of defects due to scratches, and the accuracy was improved.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.22 no.1
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• pp.125-130
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• 2013

In this paper, in order to overcome shortcomings of manual inspection for the automotive PWM Shaft, we developed an automated inline inspection system. The automated inline inspection system consists of the work feeder unit, conveying unit, outer diameter check unit, run-out and roundness check unit, machine vision, defective separation unit and status alarm unit. We used the machine vision system for automatic inspection process and designed the inline systems for automatic feeding and selecting process. Also the repeated operation test was performed in order to verify the precision and reliability of the proposed automated inline inspection system.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2003.04a
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• pp.245-250
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• 2003

Today, automobile world market is highly competitive. In order to strengthen the competitiveness, quality of automobile is recognized as important and efforts are being made to improve the quality of manufactured components. The directional ability of automobile has influenced on driver directly and hence it must be solved on the preferential basis. In the present research an automated vision system has been developed th inspect the front chassis module. To interpret the inspection data obtained for front chassis module, new interpreting algorithm have been developed. Previously the control of tolerance front chassis module was done manually. With the help of the new algorithm developed, the dimension is calculated automatically to check whether the front chassis module is within the tolerance limit or not.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.01a
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• pp.145-146
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• 2020

현재 4차 산업 혁명 시대에서 가장 중요한 화두는 빅데이터(Big Data), 인공지능이며, 이를 이용한 분야로 생산, 제조 분야에서도 인공지능 영상 인식 기술을 활용한 생산품을 자동으로 분류하고 나아가 품질검사도 할 수 있도록 개발하고 있다. 또한, 로봇을 공장의 생산라인에 운영하여 노동력 감소에 따른 보완이 되고, 제조과정의 효율성 증가와 생산시간 감소로 생산성을 높일 수 있다. 이를 위해 본 논문에서는 실시간 객체감지 기술인 YOLO-v3 알고리즘을 이용해서 PCB보드 인식, 분류할 수 있는 시스템을 개발하였다.

• 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.

• The Journal of the Convergence on Culture Technology
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• v.4 no.3
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• pp.209-212
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• 2018

Anisotropic conductive film(ACF) bonding is widely used for making fine interconnections between two different materials where soldering is not easily applicable. There are three constraints for the successful implementation of ACF bonding. A bonding contact should be pressed by a hot head with the right pressure and temperature for a pre-defined curing time. In this paper, a method for ACF bonding based on machine vision system is proposed and verified through some experiments. The system calculates the position and orientation of printed circuit boards(PCBs) on a bonding table and estimates the optimal hitting point where the hot head should be applied. Experimental results show that the proposed system achieves better adhesive strength by providing head flatness over contact surfaces.

• Proceedings of the Korean Society of Computer Information Conference
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• 2016.07a
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• pp.199-200
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• 2016

스포츠 산업 응용 분야에서 공의 실시간 품질검사는 산업 생산성과 경쟁력 견지에서 중요한 이슈이다. 컴퓨터 비전 기반 자동 품질검사는 이 문제를 해결하는 하나의 방법이다. 머신 비전 시스템은 품질관리 및 산업프로세스의 실시간 피드백을 주어, 물리적인 제한과 사람의 주관적인 판단을 극복할 수 있다. 컴퓨터비전 기반 시스템에서 디지털카메라에서 취득되는 디지털이미지는 주로 24-비트 컬러이미지이다. 이처럼 많은 레벨을 가지는 이미지분석은 복잡한 이미지 처리기법을 필요로 한다. 그러나 실시간 애플리케이션에서, 한 부분은 실시간으로 검사되어져야 한다. 이 논문에서는 최적의 임계값을 사용하여 스포츠산업계의 산업요구 사항을 만족하는 골프공 품질검사 시스템을 개발하였다.