The overall performance of a complex product generally depends on a number of specifications distributed in multi-teams from different disciplines. Multidisciplinary simulation analysis has been used widely in multidisciplinary design process. However, the knowledge discovery keeps bottleneck yet in building knowledge base for multidisciplinary design. In this paper, firstly, a framework of knowledge discovery from multidisciplinary simulation data is proposed. Secondly, a 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.
