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RISS 인기검색어
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- 저자 : 함석우 ( Seok Woo Ham ) (검색결과 3 건)
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On the Study of the Novel Type Tensile Specimen for Characterising Trixaiality of Eco-Al
함석우(Seok Woo Ham),조재웅(Jae Ung Cho),전성식(Seong Sik Cheon) Korean Society for Precision Engineering 2019 한국정밀공학회지 Vol.36 No.1
FE analyses have been performed to characterize triaxiality of Eco-Al7021-T6, one of representative environmentally-friendly materials with lightweight effect, in this study. Also, a novel shape specimen with double notched and a central hole, i.e., DNC specimen, was suggested to obtain two different values of triaxiality from one trial simultaneously. Tensile tests for the standard type specimen were conducted and true stress-true strain relationship, as well as force-displacement curve were obtained. It was revealed that two different values of triaxiality were successfully achieved from the novel DNC specimen, and the number of tests for triaxiality can be reduced with the use of the DNC specimen.
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함석우(Seok Woo Ham),김경석(Gyeong Seok Kim),전성식(Seong Sik Cheon) Korean Society for Precision Engineering 2019 한국정밀공학회지 Vol.36 No.7
Crash analyses was carried out to characterize crash behavior and injury of a wheelchair user in a train during collision based on SAFETRAIN PROJECT and AV/ST 9001, train safety regulations. There is no restraint system such as seat belt or air-bag in train, a wheelchair user is normally injured by bumping against wall. Crash behavior and injury of a wheelchair user was evaluated with respect to the distance (Dw) of the wheelchair from the wall and the wall foam thickness (tF). Additional crash analysis for a wheelchair user with seat belt was also performed in order to check the significance of the seat belt effect during crash.
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3차원 학습 데이터를 이용한 PIC 보의 강성 향상에 대한 연구
지승민 ( Seungmin Ji ),함석우 ( Seok Woo Ham ),최진경 ( Jin Kyung Choi ),전성식 ( Seong S. Cheon ) 한국복합재료학회 2021 Composites research Vol.34 No.6
Piecewise Integrated Composite(PIC) 보는 구간 조합 복합재 보로 구간 마다 적층 각도 및 순서를 다르게 적용하여 보의 강성과 강도를 향상시킬 수 있는 복합재료 보의 새로운 개념이다. 본 연구에서는 보의 거동을 고려하기 어려운 2차원 학습 데이터를 대신하여 3차원 학습 데이터가 적용된 머신 러닝 모델을 이용한 PIC 보가 제안되었다. 학습 데이터 및 훈련 데이터 셋(Training Data Set)은 지정된 참조 요소에서 3축 특성 값(Stress Triaxiality Factor)을 추출하여 세 가지 하중 유형(인장, 압축 그리고 전단)으로 분류되어 구성되었고, 이에 따른 하이퍼파라미터(Hyperparameter)가 제안되었다. 이를 통하여 예측된 PIC 보로 유한 요소 해석이 진행되었고 3차원 학습 데이터로 예측된 모델이 처짐 변형량이 감소된 것이 확인되었다. 이를 통해 3차원 학습 데이터를 이용하는 것이 경쟁력있는 것으로 판단되었고 처짐 변형량의 감소로 타당성이 검증되었다. Piecewise Integrated Composite (PIC) is a new concept to design composite structures of multiple stacking angles both for in-plane direction and through the thickness direction in order to improve stiffness and strength. In the present study, PIC beam was suggested based on 3D training data instead of 2D data, which did offer a limited behavior of beam characteristics, with enhancing the stiffness accompanied by reduced tip deformation. Generally training data were observed from the designated reference finite elements, and preliminary FE analysis was conducted with respect to regularly distributed reference elements. Also triaxiality values for each element were obtained in order to categorize the loading state, i.e. tensile, compressive or shear. The main FE analysis was conducted to predict the mechanical characteristics of the PIC beam.
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