WP1 – Digital Framework for an Interoperable Open Innovation Platform
•Definition of user requirements by making a deep analysis of the industrial pilots (T1.1) to extract the requirements for technical activities.
•Definition of the process mappings for every use case (T1.2) providing the basis for the Implementation of a Digital Pipeline tool.
•Development of the main concept of an open-innovation platform (T1.3) boosting data interoperability along the material value chain for enhancing the efficiency and quality of the production systems.
•Conceptual development of end-to end visualization tools (T1.4).
•Definition of a conceptual ontology ecosystem for ensuring the interoperability (T1.5).
WP2 – Virtual Engineering Workflows
•Definition of standards into different CAx tools for achieving data interoperability and data exchange (T2.1). In this sense, VMAP plays a crucial role in providing an open standard for reading and writing data in virtual and physical process flows.
•Extensive literature review (deposition and heat simulation, distortion models, properties prediction, validation) was performed, in order to have high-fidelity and reliable multiscale and multiphysics models able to predict defects of the material deposition during WAAM and SMC process (T2.2).
•Development of testing campaign for material characterization for the MX3D use case (T2.3). For the MAR use case, the characterization plan was also established and started to produce material properties data for simulation.
•First steps in order to standardise the documentation of the simulation and modelling workflows (T2.2) as well as the material characterization procedure (T2.3) using a formal agreement on a terminology and classification of materials models have been carried out (T2.4).
WP3 – Digital Production and Quality Assurance
•A list of variables to be measured using sensors have been already defined for the MX3D and MAR use cases (T3.1)to monitor the manufacturing processes and for the implementation of feedforward control strategies,
•First steps in the definition of the Asset Administration Shell (AAS) for the production equipment data modelling to establish manufacturing equipment digital twins have been made (T3.2).
•Moreover, several tests have been already made in order to control heat accumulation and stick out and height compensation for WAAM process in the MX3D use case. For the MAR use case, the control strategy considers the availability of temperature data from thermocouples that are installed within the mould, while leveraging the inputs from reduced order models (T3.3).
WP4 – Data-driven Intelligence for enhancing Efficiency and Quality in the Material Value Chain
•For the distributed edge and HPC-cloud based integration, a functional architecture has been defined comprising of a hybrid model that will run on the edge and fetch updates from the cloud (T4.1).
•In order to apply AI for gaining knowledge from heterogeneous data sources along the material value chain, it is very important to define the main data requirements and relevant information. In this sense, relevant information is being provided from process parameters, NDT/metrology, process clock, material and environment, quality control, sensors (T4.2).
WP5 – Industrial-driven Demonstrators
•The commissioning and deployment plans of the demonstrators for both MX3D and MAR use cases have been already defined (T5.1).
•For the impact assessment of the industrial-driven demonstrators, a first LCCA/LCA analysis is being performed, with data collected via a questionnaire sent to the two demonstrator partners (T5.2).
