인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
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.
피인용 0
초록·키워드
Purpose: This paper proposes a feature-based model to estimate lap joint crack.
Methods: First, the piezoelectric sensor data of the aluminum lap joint was subjected to signal processing to filter noise and extract the target area data. Second, three features were extracted from the filtered data. Moreover, a crack length estimation model was derived as a function of loading cycles for each feature. Finally, a crack length model as a function of loading cycles was proposed by combining the features. The performance of the proposed model is verified by comparing the result of the crack length estimation with that of the Paris-Erdogan model.
Results: The error of the proposed model against the measured values was between 0.03% and 1.8%, proving that the combined model outperforms the individual feature-based model. Additionally, the Paris-Erdogan model showed less accurate prediction than that of the proposed model.
Conclusion: This study confirms an excellent result by proposing a model with combined features for estimating the crack length of the lap joint.
Methods: First, the piezoelectric sensor data of the aluminum lap joint was subjected to signal processing to filter noise and extract the target area data. Second, three features were extracted from the filtered data. Moreover, a crack length estimation model was derived as a function of loading cycles for each feature. Finally, a crack length model as a function of loading cycles was proposed by combining the features. The performance of the proposed model is verified by comparing the result of the crack length estimation with that of the Paris-Erdogan model.
Results: The error of the proposed model against the measured values was between 0.03% and 1.8%, proving that the combined model outperforms the individual feature-based model. Additionally, the Paris-Erdogan model showed less accurate prediction than that of the proposed model.
Conclusion: This study confirms an excellent result by proposing a model with combined features for estimating the crack length of the lap joint.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
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UCI(KEPA) : I410-ECN-0101-2021-323-001550421
