Characterisation of control and quality control systems (AI based) using physically based simulation (PBS) of physico chemical processes (material transformation processes and surrounding environment) Generation of domain knowledge and sinthetic process data sets

Construction of physically based simulation models (continuous) of material transformation processes in manufacturing (for instance: injection, extrusion, stamping, peen forming, forging, RTM, 3D printing, chemical reactors, etc) and surrounding production environment (for instance: ventilation, pollutants distribution, etc.) for deep understanding of their underlaying physics (heat transfer, mechanics, fluidodynamics, chemistry, etc). The models provide to manufacturing companies and AI developers with theoretical connection between process parameters and final product caracteristics, as a source for domain knowledge and explainability for AI models. Model exploration through simulation DOEs also provide physically based synthetic data for data-driven models development and test. Additionally these models will be used for the analysis of performance. Proposal and initial evaluation of alternative, complementary or improved solutions to maximice the capabilities and perfomance of the proposed solution in objective use cases. Target audience: industrial companies – material transformation equipment developers, data-driven model developers.