The overall objective of AI REDGIO 5.0 project is to enable competitive AI-at-the-Edge digital transformation of Industry 5.0 Manufacturing SMEs. AI REDGIO 5.0 is part of the I4MS2 initiatives, which also include CIRCULOOS, WASABI, and AIRISE. These innovative projects share a common objective: to revolutionize the European manufacturing sector by enhancing the sustainability, resilience, and competitiveness of SMEs and small mid-caps.
Key partners in this initiative include:
The AI REDGIO 5.0 innovation action is conducted by a consortium of 44 Partners from 18 countries.
Politecnico di Milano: Its research group of Manufacturing will be involved in this project with its specific competences in the fields of Manufacturing. POLIMI is the AI REDGIO 5.0 coordinator bringing its yearly Project Coordination expertise to the project.
Brainport Industries: Flexible manufacturing at BPI Brainport Innovation Campus in Eindhoven is a field lab which concentrates on flexible production and assembly solutions, while also focusses on robotization of manufacturing processes.
Oost NL: DIH AI East Netherlands, DIH Boost Smart Industry and candidate EDIH and provides services to boost the digital and green transitions for SMEs.
University of Twente: Focusing on innovative solutions for digital transition in manufacturing and developing pathways towards smarter industry.
Role of the University of Twente
The University of Twente represents as didactic factory in the East Netherlands Region in the project. Core activities consist of research, demonstrators, and knowledge transfer: (i) Demonstration and showcasing; (ii) Test before invest; (iii) Prototyping; (iv) Provision of training platform. Within the project environmental sustainability through AI is key. A full-scale instrumentation will be available as well as appropriate digital infrastructure(s) bringing together data from different sources to eventually built up digital twins. Dedicated innovative solutions to address environmental targets will be developed and demonstrated.
Research methodology
A comprehensive IIoT architecture has been designed which includes the data acquisition on edge devices, data storage and cloud-based applications. Based on this IIoT architecture, we are developing the applications such as Overall Equipment Effectiveness (OEE) prediction. These applications analyse the vibration/sound sensor data using machine learning algorithms at the edge to predict the machine specific state. These applications also demonstrate for the concept of AI at the edge. A wide variety of potential use cases applications that can be developed based on this architecture by combination of different sensors and algorithms. Examples include maintenance prediction, abnormal detection, smart planning, quality control, remote operation and others.
