The University of Osaka · Engineering
이 교수의 연구실은 재료의 피로 거동을 정밀하게 기술하기 위한 비선형 순환 플라스티시티 모델 개발에 초점을 맞추고 있습니다. 특히, 단순 인장/압축 시험을 기반으로 한 기계적 특성 특성화와 함께, 피로 시험에서 나타나는 래치팅, 히스테리시스 루프 등의 현상을 정확히 반영할 수 있는 새로운 플라스티시티 이론을 개발하고 있습니다. 연구는 고주기 피로 하중 조건에서의 응력-변형률 거동을 이해하고, 기존의 탄성 영역으로 간주되던 영역 내에서도 플라스틱 변형이 발생하는 메커니즘을 해석하는 데 목적이 있습니다.
Figures are computed from collected data and may differ slightly.
Experimental observations have evidenced the insufficiency of yield surface definition via monotonic loading to guarantee adequate description of material behaviour under fatigue tests. Therefore, mechanical property characterisation through simple experimental tests such as uniaxial monotonic extension/compression is favoured, considering different responses due to cyclic loading. Herein, a phenomenological cyclic plasticity model is formulated to capture the correct generation of plastic defor
In order to describe cyclic plasticity phenomena plastic stretching within a yield surface has to be considered, whilst conventional elastoplastic constitutive equations are only capable of describing deformation behaviour for a stress path near the monotonic/proportional loading. The subloading surface model categorized in the unconventional plasticity model and describing a smooth elastic-plastic transition would be applicable to non-proportional loading process including cyclic loading behavi
In order to simulate mechanical fatigue phenomena represented by cyclic plasticity such as ratcheting and hysterisis loop, the plastic stretching within a yield surface has to be described, whilst the plastic strain is induced remarkably as the stress approaches the dominant yielding state. The traditional plastic constitutive equation, however, is capable of describing deformation behavior for the stress path only near the monotonic/proportional loading, since the inside of the yield surface is
We propose a method to identify the human face using a 3D gray scale image which combines a 3D image with a gray scale image. The proposed method can identify facial images which face in various directions. First, 3D positions, where the eyes and the tip of the nose are located, are estimated in the acquired 3D gray scale image. Next, the calibration of facial directions and brightness is performed. Due to facial images facing more to the right or the left, some parts of the calibrated data are
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