Clustering and selection of knowledge meshes of knowledgeable manufacturing systems based on decomposition of fuzzy relational data

Abstract

This article presents a study on the clustering and selection of knowledge meshes in the knowledgeable manufacturing system that transforms all types of advanced manufacturing modes into corresponding knowledge meshes and selects the best combination of knowledge meshes to satisfy enterprise requirements. The appropriate knowledge mesh for enterprises not only includes the matching of knowledge mesh function but also that of performance perfection and structure. Thus, the similarity degree of knowledge meshes, whose properties are proved to relate to the operations of knowledge meshes, is constructed from the functional matching, the perfection degree and the layer number of lowest-layer knowledge point. The similarity values, taken as cluster data, are used to construct the fuzzy relational matrix to compress the high-dimensional feature space. The decomposition of the matrix is transformed into an optimization problem solved by the gradient method. The knowledge meshes with higher membership degree in each class are taken as reference knowledge meshes to identify user’s requirements exactly. The comparison of target knowledge mesh with reference knowledge meshes definitely narrows down the knowledge mesh selection to a certain type. Based on the above, the knowledge mesh clustering and selection method is exemplified. The results show that the proposed method works well in narrowing the search range and clarifying user requirements.