인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
개인구독
소속 기관이 없으신 경우, 개인 정기구독을 하시면 저렴하게
논문을 무제한 열람 이용할 수 있어요.
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
This study proposes an eXplainable AI (XAI)-based vibration evaluation method to assess the buzz, squeak, and rattle (BSR) performance of vehicle seatbelt retractors, focusing on noise-induced anomalies. The proposed method enables the prediction of vibration characteristics from seatbelt retractor design parameters, enabling the pre-screening of potential BSR noise occurrence at the design stage. Compliance measurements were collected from 15 different vehicles, and vibration evaluation indicators (i.e., system stiffness and Q-factor) were derived to quantify dynamic response characteristics related to BSR noise. These indicators serve as key metrics for BSR-related performance evaluation, enabling defect identification based on predefined thresholds. Furthermore, an XAI-integrated machine learning model was developed to predict the system stiffness and Q-factor directly from seatbelt retractor design parameters. The predicted indicators were validated against experimental thresholds, and the Shapley additive explanations algorithm was employed to analyze the influence of design parameters on noise-induced vibration performance. By leveraging the XAI approach with the compliance data from multiple vehicle models, this paper presents a systematic methodology for BSR-focused vibration analysis and quality control of seatbelt retractors. The proposed approach enables manufacturers to predict vibration characteristics without direct measurement, enabling earlier detection of potential BSR issues and reducing reliance on costly experimental testing while ensuring product reliability.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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UCI(KEPA) : I410-151-25-02-093136218
