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권오건(Oh-geon Kwon),박훈(Hoon Park),강정태(Jeong-Tae Kang) (사)한국CDE학회 2015 한국 CAD/CAM 학회 학술발표회 논문집 Vol.2015 No.하계
Past wiring harness designs have been made primarily on the local computer. Over time, it increases the complexity of the engineering design. Adopting enterprise systems such as ERP and PLM to manage this complexity effectively but part of it solved, part of not from a view of the wiring harness characteristics. Especially regarding the harness drawings and BOM data generated by the part numbers that combining a number of options will cause storage problems. This situation was an opportunity to build a new infrastructure based wiring harness design system.
DBSCAN 기반의 제조 공정 데이터 불량 위치의 검출
최은석(Eun-Suk Choi),김정훈(Jeong-Hun Kim),아지즈 나스리디노프(Aziz Nasridinov),이상현(Sang-Hyun Lee),강정태(Jeong-Tae Kang),류관희(Kwan-Hee Yoo) 한국콘텐츠학회 2017 한국콘텐츠학회논문지 Vol.17 No.7
제조 산업은 국가 경제 성장의 원동력으로 그 중요성이 부각되고 있다. 이에 따라 제조 공정상에서 생성되는 제조 데이터 분석의 중요성 또한 조명 받고 있다. 본 논문에서는 PCB(Printed Circuit Board) 제조공정에서 발생한 로그 데이터를 분석하여 PCB 상에서 빈번하게 발생하는 고장 영역에 대해서 작업자가 고장 영역을 직접 눈으로 볼 수 있도록 시각화하는 방법을 제안한다. 우선 고장 영역을 파악하기 위해서 PCB 공정 데이터 집합에 K-means, DB-SCAN 클러스터링 알고리즘을 적용하여 군집화 하였고, 두 알고리즘 중 더 정확한 고장 영역을 도출하는지 비교하였다. 또한 MVC(Model-View-Controller) 구조 시스템을 개발하여 실제 PCB 이미지 상에 클러스터링 결과를 출력하는 것으로 실제 고장영역을 눈으로 확인할 수 있도록 시각화하였다. Recently, there is an increasing interest in analysis of big data that is coming from manufacturing industry. In this paper, we use PCB (Printed Circuit Board) manufacturing data to provide manufacturers with information on areas with high PCB defect rates, and to visualize them to facilitate production and quality control. We use the K-means and DBSCAN clustering algorithms to derive the high fraction of PCB defects, and compare which of the two algorithms provides more accurate results. Finally, we develop a system of MVC structure to visualize the information about bad clusters obtained through clustering, and visualize the defected areas on actual PCB images.
자동차 부품 생산라인을 위한 CPS 플랫폼과 디지털트윈 개발 및 적용
최종환(Jonghwan Choi),양진호(Jinho Yang),임주희(Joohee Lym),노상도(Sang Do Noh),이상현(Sang Hyun Lee),강정태(Jeong Tae Kang),이대엽(Dae Yub Lee),김형선(Hyung Sun Kim) (사)한국CDE학회 2021 한국CDE학회 논문집 Vol.26 No.4
Nowadays, many manufacturing companies design, engineer, and produce their products through a globally distributed supply chain. In the automotive industry, globalization of the supply chain is expanding and becoming more complicated under increasing global competition and demand for reduction in production costs. Under such circumstances, automotive parts manufacturers are making efforts to implement smart manufacturing (SM) system that ensures the production of products with reasonable prices and high quality, while meeting the needs of various customers. To implement SM, a production line must be smart based on data from manufacturing sites through the application and convergence of technologies such as cyber-physical system(CPS) and digital twin. With the application of such technologies, it is possible to achieve faster and more accurate decision-making and more efficient utilization of manufacturing resources. In addition, a CPS-based integrated platform is needed to link and integrate data with the distributed manufacturing environment composed of heterogeneous facilities. By utilizing this, it is possible to implement and operate a production system that can flexibly respond to various external changes. In this paper, we propose a methodology for design, implementation and application of CPS platform-based digital twin that meets global standards for SM of global automotive parts manufacturers.