An ontology-based, general-purpose and Industry 4.0-ready architecture for supporting the smart operator (Part I – Mixed reality case). (July 2022) - Longo, Francesco; Mirabelli, Giovanni; Nicoletti, Letizia; Solina, Vittorio
Abstract: The advent of novel industry 4.0-driven technologies is offering significant opportunities to manufacturing systems, but at the same time it is posing new great challenges. The growing number of connected and interconnected devices is enormously increasing the amount of data generated, which must be properly organized to give value to the business. Basically, the need for approaches that are able to guarantee compliance with FAIR data principles is significantly emerging. Recently, the KNOW4I platform has been proposed in the literature to support the smart operator through a suite of Smart Utilities and Objects (Longo et al., 2022). The main purpose of this paper is to extend such platform, in the form of an ontology-based, general-purpose and industry 4.0-ready architecture, capable of improving the capabilities of the smart operator, with a focus on mixed reality. The novel proposal is based on two fundamental aspects: (1) a new general ontology, developed through the ontology engineering methodology; (2) the adoption of FIWARE, an open-source infrastructure, capable of enabling interoperability between different systems. The proposed architecture is implemented and validated on two case studies belonging to the manufacturing sector, which respectively concern (1) scheduled maintenance and alarm management and (2) customer order management. The experimental phase shows that the architecture is able to effectively and efficiently support the smart operator. Highlights: An ontology-based, general-purpose and industry 4.0-ready architecture for the smart operator. Adoption of FIWARE for enabling interoperability between several systems. A novel general ontology for data findability and re-usability. Two case studies on scheduled maintenance and alarm management, and customer order management. Compliance with FAIR data principles.