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.
초록·키워드
The adjustment pipes are components used to connect fixed pipes where step have occurred in ship blocks. This study proposes a method for predicting the deformation of adjustment pipes that occurs during the fabrication. Thermo-elasto-plastic finite element analysis (FEA) was employed to numerically simulate deformations occurring under various welding conditions. By integrating both experimental and analytical data, a model was developed to quickly and accurately predict welding deformation and compliance with tolerance limits using machine learning algorithms. To manage the complexity of high-dimensional variables, Principal Component Analysis (PCA) was utilized, and predictive models were constructed using KNN, Decision Tree, and XGBoost algorithms. The prediction results indicated that XGBoost algorithm achieved the highest accuracy in predicting the amount of deformation, and Decision Tree algorithm excelled in predicting compliance with tolerance limits. The results of this study are expected to contribute to the design and quality control of adjustment pipes by providing a methodology that provides reliable predictions under various welding conditions.
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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