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Collaborative intelligent CAD framework incorporating design history tracking algorithm
DOI:10.1016/j.cad.2010.08.001.png)
摘要
En 中文
This paper shows a new and efficient collaborative intelligent Computer Aided Design (CAD) framework in a theoretical study. While other collaborative CAD frameworks or protocols focus on decreasing the waiting time for updating design or communication methods for design review among collaborative designers, the suggested collaborative design protocol determines the next design ownership criterion with the objective of minimizing redundant design stages and design bottlenecks using the design history. In addition, the suggested framework generates an efficient reverse-engineered process for achieving the final design target with identification of redundant designs and how these can be prevented while resolving other existing collaborative design issues. As a design history mechanism, a feature-based design history tracking algorithm is suggested. In each design stage, the modeling activities are mapped to the related geometry and topology information. This information is reasoned into features using the feature design history graph (FDHG) and modified attribute adjacency graph (MAAG). The identified features are utilized for determining the redundant design stage and how it can be changed efficiently using the tracking algorithm. As the size of the design history increases with the number of collaborative designers and their design stages, this design history mechanism contributes to a decrease in size and captures the characteristics of design using features. As possible directions for future research this suggested framework can provide an insight into the design tendencies of designers and also be used as a reference model for intelligent CAD systems with an extracted design history-based knowledge database. (C) 2010 Elsevier Ltd. All rights reserved.
Keyword:
Collaborative CAD
Intelligent CAD
Feature-based design history
Redundant design
Collaborative design protocol
期刊
C
IF:
3.1
论文数:
3.1K
被引数:
6.4K
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