Periodic Reporting for period 1 - MARS (Manufacturing Architecture for Resilience and Sustainability)
Okres sprawozdawczy: 2023-01-01 do 2024-06-30
European manufacturing SMEs represent a major pillar of the EU economy but, even though some of these SMEs are world’s champion in their own business area, they are still threatened by the lack of radical technical innovation as well as successive crises of their supply chains. The MARS project aims to remedy to both issues by enabling SMEs to access advanced European breakthrough innovations in the field of AI-driven digital manufacturing processes and enter into process chains that are geographically distributed. Specifically, by gathering diverse expertise coming from complementary European partners, MARS will develop Industry4.0 emerging technologies including digital twins of products, processes and machines, bio-intelligent production devices with local intelligence and high sensing coverage, central intelligence with fleet learning approaches, data-driven manufacturing process models from different sources, blockchain technology for data hashing, traceability and securitization, multi-agent based manufacturing planning, multi-criteria intelligent optimization of processes and resources especially addressing environmental footprint. As a result, the impact of the project will lie into introducing radical flexibility in all different aspects of manufacturing processes, in particular by redefining the process route, raw material, resources, technology, throughput, manufacturing site, delivery date in no time, while keeping up with product’s requirements, proven product quality and sustainability of both processes and products. By demonstrating its results on two case studies exhibiting advanced manufacturing processes (incl. homogeneous and heterogenous data), MARS will show how SMEs can decrease time delivery under difficult economical boundary conditions, while targeting ambitious energy-saving environmental objectives.
The activities carried out during the first period of the project mainly concern:
Defining the architecture of the MARS platform
Developing the homogeneous database
The development of optimal machine learning in the context of manufacturing, which will support predictive AI. Data acquisition was a key stage in order to enable the AI model to be trained and to ensure that the results meet the quality criteria.
Development of the neurosymbolic model has also begun and has already been presented at several conferences.
Digital certification is also being developed.
The development of federated learning for manufacturing has also begun, with the architecture completed and the first prototype developed. Work is continuing to apply federated learning in a more customised way to all scenarios. Research into other federated learning strategies is also under way.
le decentralized protocol and multi agent system testbed for production scheduling and planning has been achieved
Defining the architecture of the MARS platform
Developing the homogeneous database
The development of optimal machine learning in the context of manufacturing, which will support predictive AI. Data acquisition was a key stage in order to enable the AI model to be trained and to ensure that the results meet the quality criteria.
Development of the neurosymbolic model has also begun and has already been presented at several conferences.
Digital certification is also being developed.
The development of federated learning for manufacturing has also begun, with the architecture completed and the first prototype developed. Work is continuing to apply federated learning in a more customised way to all scenarios. Research into other federated learning strategies is also under way.
le decentralized protocol and multi agent system testbed for production scheduling and planning has been achieved
At this stage of the project, the results are not applicable