지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2021.6
- 수록면
- 108 - 114 (7page)
- DOI
- 10.33162/JAR.2021.6.21.2.108
이용수
초록· 키워드
오류제보하기
Purpose: We propose a prediction model based on the damage features for fatigue crack growth in lap joints.
Methods: Two features were extracted, and a crack quantification model was established. Each feature required to calculate the crack length was transformed to a parameter for a cycle. The model parameters were derived using Bayesian approaches through Markov chain Monte Carlo sampling, and the crack growth was predicted. The model accuracy was examined by comparing the obtained results with those derived using the Paris model.
Results: Comparison of the crack growth determined using the feature-based model and Paris model as well as the measurement data indicated that the maximum and minimum error rates for the feature-based model were 0.67% and 0.08%, respectively.
Conclusion: A model was developed to predict the crack length, which grows based on cycles. The feature-based model uses a small amount of data and can eliminate the uncertainty caused by the variability in the manufacturing of the materials.
Methods: Two features were extracted, and a crack quantification model was established. Each feature required to calculate the crack length was transformed to a parameter for a cycle. The model parameters were derived using Bayesian approaches through Markov chain Monte Carlo sampling, and the crack growth was predicted. The model accuracy was examined by comparing the obtained results with those derived using the Paris model.
Results: Comparison of the crack growth determined using the feature-based model and Paris model as well as the measurement data indicated that the maximum and minimum error rates for the feature-based model were 0.67% and 0.08%, respectively.
Conclusion: A model was developed to predict the crack length, which grows based on cycles. The feature-based model uses a small amount of data and can eliminate the uncertainty caused by the variability in the manufacturing of the materials.
목차
1. Introduction
2. Damage Features Extraction
3. Crack Growth Prognostic Using Feature based Model
4. Comparison of Paris Model and Feature based Model Results
5. Conclusion
References
참고문헌 (10)
참고문헌 신청
전현규 / 2011 / 레일손상에 의한 윤중증가를 고려한 표면균열 성장예측 / 한국정밀공학회지 28(9):1078~1085
김주희 / 2019 / LCD 패널의 응력 해석을 통한 단순화 칩핑 허용균열 길이 예측 / 대한기계학회논문집 A 43(12):857~865
박명진 / 2020 / 균열 검사 결과를 고려한 선체 잔류 피로 수명의 확률론적 예측 / 대한조선학회 논문집 57(1):1~7
W. G. Kim / 2011 / Probabilistic assessment of creep crack growth rate for Gr. 91steel / Nucl. Eng. and Des 241(9):3580~3586
이동형 / 2007 / 압입축에 발생하는 프레팅 피로균열 발생 및 진전 특성 실험 / 대한기계학회논문집 A 31(6):701~709
김주희 / 2019 / LCD 패널의 응력 해석을 통한 단순화 칩핑 허용균열 길이 예측 / 대한기계학회논문집 A 43(12):857~865
박명진 / 2020 / 균열 검사 결과를 고려한 선체 잔류 피로 수명의 확률론적 예측 / 대한조선학회 논문집 57(1):1~7
W. G. Kim / 2011 / Probabilistic assessment of creep crack growth rate for Gr. 91steel / Nucl. Eng. and Des 241(9):3580~3586
이동형 / 2007 / 압입축에 발생하는 프레팅 피로균열 발생 및 진전 특성 실험 / 대한기계학회논문집 A 31(6):701~709
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