
INOS HELLAS
INOS HELLAS
2 Projects, page 1 of 1
assignment_turned_in Project2012 - 2015Partners:ULP , GMX, AWL, FHG, Karlsruhe University of Applied Sciences +7 partnersULP ,GMX,AWL,FHG,Karlsruhe University of Applied Sciences,FG,SIG,INOS HELLAS,IEF Werner,CMF,TNX,HWHFunder: European Commission Project Code: 314329more_vert Open Access Mandate for Publications and Research data assignment_turned_in Project2020 - 2023Partners:INOS HELLAS, TEKNOLOGIAN TUTKIMUSKESKUS VTT OY, University of Belgrade, TENFORCE, IMP +6 partnersINOS HELLAS,TEKNOLOGIAN TUTKIMUSKESKUS VTT OY,University of Belgrade,TENFORCE,IMP,UL,ATC,Ibermática (Spain),INEOS SERVICES BELGIUM,IK4-TEKNIKER,CONTINENTAL FRANCE SNCFunder: European Commission Project Code: 957391Overall Budget: 5,467,700 EURFunder Contribution: 5,467,700 EURThe AI-PROFICIENT project will pave the way for integration of advanced AI technologies to manufacturing domain through an evolution from hierarchical and reactive decision making to self-learning and proactive control strategies. The proposed approach is 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 (human-in-the-loop). AI-PROFICIENT 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. Taking the full advantage of AI capabilities and human knowledge, AI-PROFICIENT will develop proactive control strategies to improve the manufacturing process over three main vectors: production efficiency, quality and maintenance. AI-PROFICIENT will increase the positive impact of AI technology on the manufacturing process as a whole, while keeping the human in central position, assuming supervisory (human-on-the-loop) and executive (human-in-command) roles. AI-PROFICIENT intends to identify the effective means for human-machine interaction, while respecting the safety and security requirements and following the ethical principles, in order to enable: event identification and prediction, operation scenarios simulation, transparent decision and optimal control, and personalized shop-floor assistance. Such an approach will ensure that the ambitious, but realistic project targets, namely to improve production planning and execution, as well as to facilitate the human-machine collaboration, are achievable for the European manufacturing and process industry. The implementation of this novel concept will be based on the results of several recently finished European R&D projects, which will be demonstrated in two pilot sites, under different scenarios of significant economic value.
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