Different sectors of manufacturing and process industry are characterized by the need for adaptive configuration of the control parameters and rapid response to fluctuating production demands. To answer the production responsiveness demands, the industry requires innovative solutions for highly adaptable and flexible production systems. This implies the need for reconfigurable/agile production systems, relying on, in the early production stage, fast ramp-up processes to reach full production capacity for new products and, in the mature production stage, an optimal operation execution at the plant level, while considering component-level degradation and required maintenance. To cope with such a challenge, introduction of advanced AI technologies in manufacturing plants is a must. Digitalization of factories is bringing several technological advances that facilitate the fulfilment of these requirements. To start with, there is an increased amount of sensor data (i.e. IIoT) and operation information that can now be deeply explored and exploited by AI systems: from reactive to proactive management. Furthermore, in order to embrace the upcoming Industry 5.0 it is necessary to enable effective ways for human intelligence to work in harmony with AI systems.
In line with these AI-based production challenges, the AI-PROFICIENT project which is a Research and Innovation Action (RIA), aspires to bring advanced AI technologies to manufacturing and process industry, while improving the production planning and execution, and facilitating the collaboration between humans and machines (Ethics by design approach). So, main objective is to bring the advanced AI technologies to manufacturing domain through an evolution from hierarchical and reactive decision making to self-learning and proactive control strategies, underpinned by predictive and prescriptive AI analytics at both component and system level, by cross-fertilizing edge and platform AI, while leveraging the human knowledge and feedback for reinforcement learning. This project belongs to the ICT-38 cluster (
https://ai4manufacturing.com(se abrirá en una nueva ventana)) grouping together all the projects accepted in this call.
The AI-challenges focused are refined in 3 more precise General Objectif (GOs) and 6 Scientific/Technical Objectives (STOs). These objectives served as a guideline for the project development by referring to Project Requirements, User Requirements, Functional Requirements, Functionalities and AI-services. This development is aligned with 8 WPs. In addition to demonstrate improved performance through AI-services and technologies, 3 different pilot sites have been chosen: Continental Sarreguemines (combiline machine), Ineos Geel and Cologne (production of polypropylene and polyethylene polymers). In relation to these three pilots and the WPs structuring, the project has been constructed on a mapping of the WPs to different Use Cases (UC) well representative of specific current production/maintenance/quality situations to be improved by AI. These improvements are more concretely related to 8 UCs: 5 for CONTI and 3 for INEOS (INEOS 1 and 2 at Geel Plant; INEOS3 at Cologne plant).
