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허성필(Sungpil Heo),양원호(Wonho Yang),정기현(Kihyun Chung) 한국자동차공학회 2002 한국 자동차공학회논문집 Vol.10 No.1
It has been reported that cracks in mechanical joints is generally under mixed-mode and there is critical inclined angle at which mode I stress intensity factor becomes maximum. The crack propagates in arbitrary direction and thus the prediction of crack growth path is needed to provide against crack propagation or examine safety. In order to evaluate the fatigue life of cracks in mechanical joints, horizontal crack normal to the applied load and located on minimum cross section is major concern but critical inclined crack must also be considered. In this paper mixed-mode fatigue crack growth test is performed for horizontal crack and critical inclined crack in mechanical joints. Fatigue crack growth path is predicted by maximum tangential stress criterion using stress intensity factor obtained from weight function method, and fatigue crack growth rates of horizontal and inclined crack are compared.
김철(Cheol Kim),양원호(Wonho Yang),석창성(Changsung Seok),허성필(Sungpil Heo) 한국자동차공학회 2001 한국 자동차공학회논문집 Vol.9 No.6
The hole-drilling method makes a little hole through the metal surface that has residual stress and measures the relieved stress with a strain gage. It is used widely in measuring the residual stress of surfaces. In this method, the inclined hole is one of the source of error. This paper presents a finite element analysis of influence of the inclined hole for the uniaxial residual stress field. The stress differences between measured and applied residual stress increase proportionally to inclined angle of the hole. The correction equations which easily obtain the residual stress taking account of the inclined angle and direction are derived. The measurement error of stress due to the inclined hole can be reduced to around I % through this study.<br/>
딥러닝 기반의 최대응력과 위치 예측 기법: 로드 휠 충격 테스트 예시
진아현(Ah-hyeon Jin),이성희(Sunghee Lee),유소영(Soyoung Yoo),신승연(Seungyeon Shin),김창곤(ChangGon Kim),허성필(Sungpil Heo),강남우(Namwoo Kang) 대한기계학회 2021 대한기계학회 춘추학술대회 Vol.2021 No.8
The impact test of road wheels is one of the important tests to ensure the safety of the wheels. Automakers use CAE simulation to analyze the location and magnitude of maximum stress, reducing prototype testing costs and time. However, the time required for modeling and analysis is still large, so it is difficult to quickly evaluate a large amount of conceptual design. In addition, it is difficult for general engineering designers to utilize because it requires high expertise in CAE. This study develops an AI-based wheel performance evaluation process that uses deep learning with CAE data to learn the location and magnitude of maximum stress. This deep learning model can predict the strength performance of road wheels in real time, allowing rapid evaluation at the conceptual design stage without domain knowledge.