Multi-dimension density-based clustering supporting cloud manufacturing service decomposition model

Abstract

Recent years, the research on Cloud Manufacturing (CMfg) has developed extensively, especially concerning its concept and architecture. Now we propose to consider the core of CMfg within its operating model. CMfg is a service platform for the whole manufacturing lifecycle with its countless resource diversity, where organization and categorization appear to be the main drivers to build a sustainable foundation for resource service transaction. Indeed, manufacturing resources cover a huge panel of capabilities and capacities, which necessarily needs to be regrouped and categorized to enable an efficient processing among the various applications. For a given manufacturing operation e.g. welding, drilling within its functional parameters, the number of potential resources can reach unrealistic number if to consider them singular. In this paper, we propose a modified version of DBSCAN (Density-based algorithm handling noise) to support Cloud service decomposition model. Beforehand, we discuss the context of CMfg and existing Clustering methods. Then, we present our contribution for manufacturing resources clustering in a CMfg.