Since September 2015 and for a total duration of 36 months, OCME has been chosen to take part as an industrial partner to a research project financed by the European Union, in the context of Horizon 2020 program, with a total budget of 4.1 million euro.
About 113 projects were proposed but only 7 of them were selected to be part of the project and to get the fund.
The project named “IMPROVE” - which stands for Innovative Modelling Approaches for Production Systems to Raise Validatable Efficiency- benefits from the participation of 13 organizations including leading research centers, as well as medium to large companies, and 7 academic members such as the renowned Scientific Advisory Board.
From the 7th to the 10th of March 2016, OCME will host a delegation of the European project partners, at its Parma headquarters.
In this regard, Oscar Gerelli, Software Designer from OCME Research and Development department and active member of the project, explains further:
What will be the role of OCME in this project?
“OCME contributes to the project by bringing its know-how and leadership experience in the field of design and construction of machines and automatic systems, acting, among other things, as a team leader in the final prototyping activities. The project objectives are ambitious, but at the same time fundamental, in an industrial context such as ours, which has seen a silent yet profound revolution in these recent years; terms such as "Industry 4.0", "Internet of Things" and "Factory of the Future" express increasingly rooted concepts. The IMPROVE study will lead to remarkable progresses within the industry and it is thanks to this close connection with the production world that the project has received such a positive evaluation and feedback.”
In this project no less than 13 organizations are involved: what are the goals you set up for this collaboration?
We are cooperating in order to:
• Create an intelligent support system to the end user, able to mask or hide the increasing complexity of automatic machines
• Create a reliable "virtual factory" that reflects the behavior of the "physical"one
• Apply the most innovative self-learning techniques for modelling complex physical systems, minimizing the need for manual formalization
• Use the "virtual factory" within simulation contexts, as well as for the performance optimization of production lines, both aimed at the predictive analysis diagnosis
• Analyze the impact on the end users of previous socio-technical activities