Digital twins bringing agility and innovation to manufacturing SMEs, by empowering a network of DIHs with an integrated digital platform that enables Manufacturing as a Service (MaaS)

Información del proyecto

DIGITbrain

Identificador del acuerdo de subvención: 952071

DOI

10.3030/952071

Proyecto cerrado

Fecha de la firma de la CE 2 Julio 2020

Fecha de inicio 1 Julio 2020

Fecha de finalización 31 Diciembre 2023

Financiado con arreglo a

• INDUSTRIAL LEADERSHIP - Leadership in enabling and industrial technologies - Information and Communication Technologies (ICT)

Coste total

€ 9 392 812,09

Aportación de la UE

€ 8 340 511,48

Coordinado por

PNO INNOVATION SRL
Italy

Periodic Reporting for period 3 - DIGITbrain (Digital twins bringing agility and innovation to manufacturing SMEs, by empowering a network of DIHs with an integrated digital platform that enables Manufacturing as a Service (MaaS))

Período documentado: 2023-01-01 hasta 2023-12-31

Resumen del contexto y de los objetivos generales del proyecto

The manufacturing industry is constantly evolving; customers have new requirements and want more personalisation; interoperability with new products and technologies is expected; regulations and norms are becoming stricter; the environment calls for more protection; the aging of the workforce jeopardises the collective know-how. These aspects cause increasing pressure that the manufacturing industry needs to address. Manufacturing SMEs are especially challenged because of the limited resources and difficulty accessing digital technologies and advanced manufacturing hardware tailored to their needs.
Digital Twins are a way to answer these challenges. However, implementing Digital Twins is yet another challenge for many manufacturing SMEs since it requires a lot of expertise and a holistic approach ranging from the manufacturing machines to become data resources, to data-driven and/or physically-based modelling, to mastering data lakes and compute resources such as HPC, Cloud and Edge Computing.
The DIGITbrain project conceptualised the DIGITbrain Solution with its Digital Product Brain (DPB) to facilitate Manufacturing as a Service (MaaS). The DPB guides the behaviour and performance of an Industrial Product over its lifecycle. The DPB captures knowledge about the evolution of Industrial Product Instances, such as instances of an Industrial Product Family installed on a production site. Industrial Products (mechatronic systems or manufacturing machines) and their behaviours are represented by Digital Twins that coalesce their physical and digital dimensions.
The DIGITbrain project aims to extend the traditional Digital Twin concept towards the DPB that memorises the events that occurred (physical and digital) throughout its entire lifecycle. By separating the assets constituting Digital Twins, namely Data, Model, and Algorithm, together with Model-Algorithm Pairs and Microservices, DIGITbrain aims to build reusable building blocks to ease and accelerate the creation of Digital Twins and allow for the commercialisation of these assets independently from each other via the Digital Agora.

Trabajo realizado desde el comienzo del proyecto hasta el final del período abarcado por el informe y los principales resultados hasta la fecha

DIGITbrain has achieved its initial six main project objectives:
1) Implement the Digital Brain concept by configuring and orchestrating Data/Models/Algorithms (DMA) and Resources: All required concepts have been implemented. The Conditions and Messages have been fully integrated and tested, and all phases of the DMA life cycle have been covered.
2) Extend the CloudiFacturing platform by integrating the new execution engine and FIWARE industrial platform: 34 data sources were integrated. The IoTs and Sensors have been integrated via FIWARE, and the models and algorithms are available for MaaS support in emgora.eu
3) Develop the MaaS business model by empowering DIHs to become brokers of manufacturing capacity: An agreement on the Db MaaS definition/model has been reached, and the collaboration framework with the DIH was established. Two refined use cases have been implemented to foster users growth through DIHs.
4) Augment the marketplace's capabilities by integrating the Digital Brain concept and MaaS business model: At the end of the project, 40 apps, 65 processes and 72 behaviours are available for SME and DIH, as well as 31 technology providers and 208 registered users. Moreover, the registration of equipment and facility and the possibility of getting the operators’ contact details are available.
5) Conduct three waves of experiments to validate the project results: Three waves of experiments (1 internal and 2 Open Calls) have been conducted, and all their phases (design, implementation, and integration into the Db testbed) have been successfully completed.
6) Evangelise the manufacturing community by showing the approach and benefits of Db technology and MaaS and attracting new adopters: Workshops, training, and webinars have been organised, including the Db final event. DIGITbrain has actively participated in events and has a social media presence. Moreover, 22 scientific papers have been published.
The business model consultancy has supported all the experiments. Furthermore, information from the experiments was collected and analysed. The Value Proposition Canvas for each target customer identified (DIHs, ISVs, SMEs) was implemented. The experiment results contributed to additional assets for Db Digital Agora. Further investigation on the economic flows generated by the Db Solution and the MaaS implementation has been executed. The DIGITbrain business model has been defined and validated. Moreover, the exploitation strategy has been designed.

Avances que van más allá del estado de la técnica e impacto potencial esperado (incluida la repercusión socioeconómica y las implicaciones sociales más amplias del proyecto hasta la fecha)

The DIGITbrain approach has combined the physical and digital dimensions during the whole industrial product lifecycle, making use of advanced data collection and analysis techniques, including models, algorithms, and resources to empower manufacturing providers with an adaptive capacity to optimise the behaviour and performance of the instances of industrial products, by considering the individual context and operating conditions, enabling manufacturing providers to offer their solutions as a service, guaranteeing optimal operation and workload.
The participation of DIHs facilitated widespread coverage to engage the manufacturing community, thus boosting the adoption of the Db Solution. DIGITbrain has converted the CloudiFacturing Solution and ecosystem towards the requirements of MaaS, utilising the Digital Brain and its underlying technology components as the project’s main innovation.
DIGITbrain realises an ambitious technological vision by developing the Digital Brain concept and its enabling technologies. It assures that these technological advances are taken up by a critical mass in the manufacturing sector, especially by SMEs and mid-caps, resulting in the smart business model MaaS, facilitated by a network of interconnected DIHs.
DIGITbrain unleashes novel and smart business models by combining two main innovations: the Digital Brain concept in which the whole lifecycle of an Industrial Product, both from a physical and a digital perspective, is captured, and the creation of local efficient value networks by matching supply and demand of advanced manufacturing technologies.
DIGITbrain benefits not only manufacturing SMEs and mid-caps but also technology providers, DIHs, and, in general, the EU population that will benefit from advanced manufacturing solutions. Moreover, DIGITbrain contributes to improving the EU manufacturing performance, reducing delivery times and thus customer satisfaction, while better control of manufacturing processes and the efficient use of resources to foster sustainable design and more eco-friendly Industrial Products.
Finally, the implementation of the ambitious and ground-breaking Db concept is generating breakthrough knowledge, resulting in 22 scientific publications so far.

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