인공신경망을 이용한 저주기 피로수명 예측Low Cycle Fatigue Life Estimation using Artificial Neural Network
- Other Titles
- Low Cycle Fatigue Life Estimation using Artificial Neural Network
- Authors
- 이상민; 최완규; 김종천; 이정석; 박종천; 김태원
- Issue Date
- Nov-2019
- Publisher
- 대한기계학회
- Keywords
- 오스테나이트계 스테인리스강(Austenitic stainless steel); 저주기 피로(Low cycle fatigue); 피로수명예측(Fatigue life prediction); 등방연화지수(Isotropic softening factor); 인공신경망(Artificial neural network)
- Citation
- 대한기계학회 2019년 학술대회, pp.1788 - 1791
- Indexed
- OTHER
- Journal Title
- 대한기계학회 2019년 학술대회
- Start Page
- 1788
- End Page
- 1791
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/11669
- Abstract
- The traditional approach of fatigue life assessment uses Palmgren-Miner Rule as its base. This paper proposes a new method by observing change in material behavior to predict fatigue life. For experiment, austenitic stainless-steel sample was subjected to low cycle fatigue of 0.4% and 0.5% strange range. Towards fatigue life, the material displayed a tendency to soften regardless of strain range. This tendency was characterized as I (Isotropic Softening Factor) and put in to an artificial neural network designed to predict remaining fatigue life. Compared to conventional regression methods, the method proposed in this paper proved to be more accurate by up to 0.171 in coefficient of determination. Also, the returned model was tested in goodness-of-fit through adjusted R² and Shpiro-Wilk test. The results showed that the modeling method proposed in this paper could be utilized to predict low cycle fatigue life with high accuracy.
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