Title :
Data Mining Approach for Ship Virtual Assembly Based on Rough Set Theory
Author :
Li, Cuiling ; Zhao, Rongyong ; Xiang, Yang ; Xu, Shanlin
Author_Institution :
Electr. Eng. & Autom. Dept., Shanghai Maritime Univ., Shanghai, China
Abstract :
To discover process planning knowledge for ship virtual assembly, a novel data mining approach is proposed based on rough set theory. The present achievements about data mining with rough set theory and virtual assembly technology for shipbuilding are studied respectively. The current Petri net model for ship assembly is analyzed. A new transform model from Petri net to rough set theory is presented. Thereby a systematical approach is proposed based on the present process planning platform for shipbuilding and three dimension virtual assembly software. Finally, a data mining example from engineering of ship engine assembly illustrates that this approach is feasible. Some meaningful assembly knowledge is discovered. The dependence on operational experience of human-being can be reduced and the efficiency of ship virtual assembly can be enhanced obviously.
Keywords :
Petri nets; assembling; data mining; production engineering computing; rough set theory; shipbuilding industry; 3D virtual assembly software; Petri net model; data mining approach; process planning knowledge discovery; rough set theory; ship engine assembly engineering; ship virtual assembly; shipbuilding; transform model; Assembly; Computer aided manufacturing; Computer integrated manufacturing; Data mining; Design automation; Marine vehicles; Process planning; Set theory; Virtual environment; Virtual reality; Petri net; data mining; rough set theory; shipbuilding; virtual assembly;
Conference_Titel :
Computational Intelligence and Security, 2008. CIS '08. International Conference on
Conference_Location :
Suzhou
Print_ISBN :
978-0-7695-3508-1
DOI :
10.1109/CIS.2008.126
