DIGITbrain is a project deeply rooted in the innovation ecosystem of the I4MS project CloudiFacturing and the industrial platforms FIWARE and IDS, and it will build on these results, by means of extending the CloudiFacturing solution with an augmented digital-twin concept called “Digital Product Brain” (DPB) and a smart business model called “Manufacturing as a Service” (MaaS).
By having access to on-demand data, models, algorithms, and resources for industrial products (i.e. mechatronic systems supporting the production of other products), the DBP will enable their customisation and adaptation according to individual conditions. The availability of industrial-product capacity will facilitate the implementation of MaaS, which will allow manufacturing SMEs to access advanced manufacturing facilities within their regions or to distribute their orders across different ones.
The aim of HUBCAP is to deliver a vision of a sustainable network of SMEs, DIHs and other actors that enables and encourages suppliers and users of CPS models and MBD tools to meet and collaborate. Working with the hubs, SMEs will be able to access central funding for experiments via open calls as well as direct support. Members will make models, tools, training materials and expertise available to each other, either freely or on a commercial basis, making it faster and easier to access MBD CPS engineering tools. This ecosystem is supported by means of a collaborative platform.
The project aims to contribute for the optimization of the complete product-production cycle by having real-time information about machines, production process, product and perturbations into a platform that allows coping with uncertainties for the automatic scheduling and control. To achieve this optimization, an integrated multi-paradigm modelling and simulation platform has been used to evaluate key parts of the product-production cycle. On this basis, a realistic small-scale prototype system was constructed and coupled with the co-simulation to assess the inner complexity to engineer CPS-based production systems.