Industry 4.0 uses a set of technologies and organizational measures that favor automated, resilient, flexible and interconnected industrial production.
The use of smart grids, sensors and artificial intelligence allows you to have both prescriptive information aimed at improving processes and predictive information for planning maintenance, optimizing plant downtime, eliminating timing, minimizing and reducing accidents, improving quality of people’s work.
Industry 4.0 is the “Digital Transformation” of production processes and value creation in the manufacturing sector.
It uses advanced IoT sensors interconnected to machines that can be both new generation and existing connected to an intelligent network that allows you to monitor performance and promptly intervene in case of anomalies.
The claim is to have more efficient and more reliable networks, productions that never stop, flexible and non-rigid assembly lines, remote and interconnected supervision, distributed maintenance personnel, improvement of the well-being and quality of people who work with a greater attention to their safety.
Smile (Smart Monitoring IoT Learning Ecosystem) is an innovative ICT (Information and Communication Technologies) research and development project focused on the “Intelligent Factory”, capable of significantly improving and making production processes highly efficient, safe and sustainable through a Cloud platform.
The Project, whose total value exceeds one million and 300 thousand Euros (1,340,854.38 €), was developed by T.net thanks also to the concessions (530,649.35 € out of € 687,639.38) received under the OPN (Operational Program National) “Businesses and Competitiveness” 2014-2020 of the ERDF (European Regional Development Fund), and in partnership with some research groups of the DIEEI (Department of Electrical, Electronic and Computer Engineering) of the University of Catania and the CNIT (National Interuniversity Consortium for Telecommunications).
The project, which will allow us to move towards a model, both in technical and business terms, of the process-independent monitoring and prescriptive analysis as a service type, is mainly based on: