Special Issue of the Society of Naval Architects of Korea (대한조선학회 특별논문집)
- 2013.12a
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- Pages.35-41
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- 2013
Development of Artificial Intelligence Modeling System for Automated Application of Steel Margin in Early Modeling Process using AVEVA Marine
AVEVA Marine 강재마진의 선모델링 자동반영을 위한 인공지능 모델링 시스템 개발
- Kim, Nam-Hoon (CAD Development Team, Hyundai Samho Heavy Industry Co., Ltd.) ;
- Park, Yong-Suk (CAD Development Team, Hyundai Samho Heavy Industry Co., Ltd.) ;
- Kim, Jeong-Ho (Structure Design Dep't, Hyundai Samho Heavy Industry Co., Ltd.) ;
- Kim, Yeon-Yong (Structure Design Dep't, Hyundai Samho Heavy Industry Co., Ltd.) ;
- Chun, Jong-Jin (Hull Development Team, Infoget System Co., Ltd.) ;
- Choi, Hyung-Soon (Hull Development Team, Infoget System Co., Ltd.)
- 김남훈 (현대삼호중공업(주) CAD개발팀) ;
- 박용석 (현대삼호중공업(주) CAD개발팀) ;
- 김정호 (현대삼호중공업(주) 구조설계부) ;
- 김연용 (현대삼호중공업(주) 구조설계부) ;
- 천종진 (인포겟시스템(주) 선체개발팀) ;
- 최형순 (인포겟시스템(주) 선체개발팀)
- Published : 2013.12.30
Abstract
Nowadays, automated modeling system for steel margin based on interactive user interface has been developed and applied to the production design stage. The system could increase design efficiency and minimize human error owing to recent CAD technique. However, there has been no approach to the pre-nesting design stage at all in early modeling process especially where ship model should be handled at more than two design stages using AVEVA Marine. A designer of the design stage needs artificial intelligence system beyond modeling automation when 3D model must be prepared in early modeling process using AVEVA Marine because they have focused on 2D nesting traditionally. In addition, they have a hard time figuring out the model prepared in previous design stage and modifying the model for steel purchase size in early modeling process. In this paper, artificial intelligence modeling system for automated application of steel margin in early modeling process using AVEVA Marine is developed in order to apply to the pre-nesting design stage that can detect effective segments before a calculation to find if a segment locates near block butt boundaries by filtering noise segments among lines, curves and surface intersections based on IT big data analysis.
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