ROUGH SET-BASED DESIGN RULE SELECTION FOR COLLABORATIVE ASSEMBLY DESIGN

While the modern product development requires more knowledge-intensive and collaborative environment, the capture, retrieval, accessibility, and reusability of that design knowledge are increasing critical. In this paper, a rough set theory generates demanded rules and selects the appropriate minimal rules among the demanded design rules associated to the assembly design knowledge. The design rules are infrequently captured and often ignored due to its complexity. Rough set theory synthesizes approximation of concepts, analyzes data by discovering patterns, and classifies into certain decision classes. Such patterns can be extracted from data by means of methods based on Boolean reasoning and discernibility. This paper shows the feasibility of rough-set based rule selection considering complex design data objects in order to obtain efficient assembly design decision.