Systematic method for big manufacturing data integration and sharing

Abstract

Manufacturing data integration and sharing (MDIS) is an essential and key technology in big data-driven intelligent manufacturing mode. The preconditions of MDIS are generating product life cycle scenarios; strategy for acquiring data and using service according to generated scenarios to balance the interests of user, manufacturer, and environmental impacts; and standardization of data services. Firstly, this paper discusses integration process within enterprise from internal equipment-cell-shop-plant-enterprise then to external cloud. According to the different scenarios or phases, three kinds of MDIS methods are proposed, i.e., physical centralization by merging multiple data sources into an unique source for ensuring correctness of meta or general data, physical centralization by maintaining multiple data sources for promoting composed service of heterogeneous or various thematic data, and logic centralization by developing data directory for ensuring private data security and department or enterprise interests. Then, a hybrid manufacturing cloud architecture is proposed, and local critical data safely managed through private cloud, external required data, or its own provided services available through public cloud. Finally, taking machine tool and magnetic bearing resources as an example, a unified service modeling methods based on semantic ontology are used to facilitate the interconnection and interoperability between cyber space and physical space.