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새로운 한국 측면충돌 신차안전도 평가방법 변경에 따른 차체 거동에 관한 연구
김승진(Seungjin Kim),양희종(Heejong Yang),김인규(Inkyoo Kim),김용석(Yongsuk Kim) 한국자동차공학회 2014 한국자동차공학회 학술대회 및 전시회 Vol.2014 No.11
Recently occupant protection is becomes important of each country, the New Car Assessment Program tests are to be strengthened. Korea NCAP and Euro NCAP crash test of the enhanced regulation will apply in 2015. The weight of AEMDB is 350kg heavier and 200mm wider than the Progressive MDB. This paper is purposed to analyze dynamic body deformation comparison between Korea NCAP side crash test with progressive MDB and AE-MDB. Through this analyze try to find out the relationship between the vehicle deformation and Occupant Injury
AE-MDB 대응을 위한 Side Structure 민감도 분석 및 B-pillar 최적 설계
김승진(Seungjin Kim),양희종(Heejong Yang),박준성(Joonsung Park) 한국자동차공학회 2013 한국자동차공학회 학술대회 및 전시회 Vol.2013 No.11
This paper presents the optimization of B-pillar structure to meet the Euro NCAP side crash test with AE-MDB. To reduce the occupant injury, vehicle structure and restraint system should be optimized. In the side crash test, B-pillar is one of the main part of load path and main factor to increase the occupant injury. The objective of this paper is structure optimization of the B-pillar to archive high rating in the Euro NCAP side crash test with AE-MDB.
실시간 공정 모니터링을 통한 제품 품질 예측 모델 개발
오영광(YeongGwang Oh),박해승(Haeseung Park),유아름(Arm Yoo),김남훈(Namhun Kim),김영학(Younghak Kim),김동철(Dongchul Kim),최진욱(JinUk Choi),윤성호(Sung Ho Yoon),양희종(HeeJong Yang) 대한산업공학회 2013 대한산업공학회지 Vol.39 No.4
In spite of the emphasis on quality control in auto-industry, most of subcontract enterprises still lack a systematic in-process quality monitoring system for predicting the product/part quality for their customers. While their manufacturing processes have been getting automated and computer-controlled ever, there still exist many uncertain parameters and the process controls still rely on empirical works by a few skilled operators and quality experts. In this paper, a real-time product quality monitoring system for auto-manufacturing industry is presented to provide the systematic method of predicting product qualities from real-time production data. The proposed framework consists of a product quality ontology model for complex manufacturing supply chain environments, and a real-time quality prediction tool using support vector machine algorithm that enables the quality monitoring system to classify the product quality patterns from the in-process production data. A door trim production example is illustrated to verify the proposed quality prediction model.