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자동차 부품 금형 개발 프로세스의 품질지식 정보 관리체계 연구
남성호(Sung-Hon Nam),정홍진(Hong-Jin Jeong),김보현(Bo-Hyun Kim),정소영(So-Young Jung),신정훈(Jung-Hun Shin) (사)한국CDE학회 2011 한국 CAD/CAM 학회 학술발표회 논문집 Vol.2011 No.1
The Requirements of Auto Parts Companies are continuous quality activities about customers’ expectations and auto makers as part of the company’s quality assurance policy to ensure quality. The secure and Implementation of quality management process for customer satisfaction are advanced, but customers still are demanding on higher quality levels. APQP(Advanced Production Quality Planning) for more sources quality and design·developing quality standards process are structured. Companies, however, are still using quality standards which are for the nominal and hand over the document’s activities by manual labor. So customer has been dissatisfied about quality. In this paper, we study quality information management system of molding development process to enhance development-quality between assembler automobile companies and supplier automobile companies in automotive industry. First of all, APQP process are deployed to WBS(Work Breakdown Structure) for information management system through information management system based on process. Second of all, a corporate management techniques used in companies extracts factors affecting quality and structure pool. Finally, Comparative analysis between factors affecting quality extracted through APQP process which is deployed by using WBS and factors affecting quality pool derived from quality management techniques should be complementary. The factors affecting quality analyzed and improved are stored in Quality Master DB. Later, a similar development process and quality control technique are utilized in process, so that quality improvement and nonconforming product traceability can be contributed.
최헌종(Hon-Zong Choi),박정호(Jung-Ho Park),남중종(Jung-Jong Nam),남성호(Sung-Ho Nam),황정호(Jung-Ho Hwang) 대한기계학회 2004 대한기계학회 춘추학술대회 Vol.2004 No.11
This paper presents collaboration model for information system of engineering collaboration Hub system for mold industry, the e-Manufacturing Hub System. It is shown how the collaboration ,between companies in the field of the mold industry, can progressed by collaboration templates of engineering collaboration Hub system. The e-Manufacturing Hub System, is planned and driven by Korean Government and KITECH, has a goal to establish online engineering collaborative value chain in a consortium of small and medium mold related companies. We have researched the collaboration model of this consortium for the use of the e-Manufacturing Hub System. In this paper, some templates for managing internal engineering data and job structure in the viewpoint of individual company and some templates for sharing engineering data and co-work between each other companies.
김광민(Kwang-Min Kim),남성호(Sung-Ho Nam),이석우(Seok-Woo Lee),최헌종(Hon-Zong Choi) 한국자동차공학회 2004 한국자동차공학회 춘 추계 학술대회 논문집 Vol.- No.-
The aim of this study is to investigate the method optimizing processes for minimizing machining time and improving production quality of the reactor part. Increasing cutting speeds and feed rates helps the machining process to be improved. However, in order to improve of efficiency of the process as well as quality of the machined parts, a number of drawbacks should be first identified, including the work holding strategy and the selection of the optimized machining conditions. In this regards, the machining characteristics and the influence of the clamping force on work deformation have been investigated to improve the production process of the reactor part.
고속가공에서 2중 신경망을 이용한 표면거칠기 예측과 가공DB 구축 효율화 방안
원종률(Jong-Youl Won),남성호(Sung-Ho Nam),유송민(Song-Min Yoo),이석우(Seok-Woo Lee),최헌종(Hon-Zong Choi) 한국생산제조학회 2004 한국생산제조시스템학회 학술발표대회 논문집 Vol.2004 No.10
In this paper, a double artificial neural network (ANN) approach and the efficient machining database building scheme are presented for the prediction of surface roughness in high-speed machining. In this approach, 4 machining parameters and used for the prediction of cutting force components, and the combinations of 4 parameters and the predicted cutting force components are finally used for the prediction of surface roughness. The experimental results comparing the these results with the predicted values using simple 4 input nodes have been also investigated to verify the effectiveness of the proposed approach.