Akademik Veri Yönetim Sistemi
Tümer M. B. (Yürütücü)
UFUK 2020 Projesi, 2015 - 2018
Facing increased global competition, the manufacturing industry depends on high-level solutions to ensure excellent machine functionality. Current analyses estimate that system downtimes and component breakdowns lead to an energy waste of 33% in the production and a significant loss of profits. At the same time, the complexity of production plants is steadily rising due to increasing product variances, product complexity, and pressure for production efficiency. Production systems must therefore evolve rapidly and operate optimally, which creates challenges for larger industries and especially for small and medium-sized enterprises (SMEs).
To meet these challenges, the European research and innovation project IMPROVE joins forces from 13 leading players in the field of academia, industry, and software development from Europe and beyond. Together, they have developed novel data-based solutions to enhance machine reliability and efficiency. Innovative tools in the fields of simulation & optimization, condition monitoring, alarm management, and quality prediction provide manufacturers with a human machine interface (HMI) and decision support system (DSS) to ensure best possible user support.
IMPROVE’s solutions can be standardised, commercialised, made accessible and applicable for European SMEs by tackling the problem of user support functions in terms of self-diagnosis and self-optimisation. Alternatively to relying on human expertise and engineering to formulate necessary knowledge, data-driven models are used for self-diagnosis and optimisation of production plants. New self-learning tools extend capacities of manual creation, making it possible to learn accurate virtual factory models of complex, large, and distributed plants through use of real time analytics.