Combining rough set and case based reasoning for process conditions selection in camshaft grinding
Case Based Reasoning (CBR) is a novel paradigm that uses previous cases to solve new, unseen and different problems. However, redundant features may not only dramatically increase the case memory, but also make the case retrieval more time-consuming. Furthermore, camshaft grinding process is controlled by a number of process parameters, and it is more complex comparing with the ordinary cylindrical grinding. The process conditions are achieved by skilled and professional workers. Therefore, this research combines Rough set (RS) and CBR for process conditions selection in camshaft grinding, and Genetic Algorithm (GA) is developed to discretize condition features. Through the approach an optimal subset of process conditions can be selected quickly and effectively from a large database with a lot of cases, and complexity of computation of the similarity testing is significantly reduced. Moreover, the validity of the proposed solution is verified by the application of practical experiments for the process conditions selection in camshaft grinding.
rough set case based reasoning camshaft grinding genetic algorithm case reclassify feature reduction case evaluation