Knowledge Discovery from Multidisciplinary Simulation to Support Concurrent and Collaborative Design

Knowledge-based engineering (KBE) and simulation analysis have been used widely in multidisciplinary concurrent and collaborative design process. However, the acquisition of knowledge keeps bottleneck yet in building knowledge base in KBE. In this paper, a framework of knowledge discovery from multidisciplinary simulation data is proposed. Correspondingly, a data mining algorithm named fuzzy-rough algorithm is developed to deal with the simulation data by combining the fuzzy set theory and rough set theory. The proposed knowledge discovery process is applied respectively to obtain some useful, implicit production rules with efficient measure. Finally, the method is demonstrated by a metal forming simulation problem. The results prove that knowledge discovery from simulation data is feasible, and the proposed method can be applied in other disciplinary simulation