Clustering and selection method of shaft part manufacturing services on cloud platform

摘要

Under the cloud mode, it is difficult to select an appropriate shaft part processing service from the rich and huge cloud manufacturing services. The K-means algorithm and the extension theory were applied into the cloud platform to solve this problem. The similarity-based clustering method was proposed to preprocess the shaft part processing cloud services and generate a plurality of class clusters. Based on the extension theory, the matter-element models of service requirement and class clusters of shaft part processing cloud services were established, and the selection method of shaft part processing cloud service sets was presented. The result of the study shows that the clustering and selection method of shaft part manufacturing services on cloud platform is feasible and effective.