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UMLP in RAIDO

Partner

Marie and Louis Pasteur University embodies a new era for higher education and research in the Bourgogne-Franche-Comté region. 

Born from the transformation of University of Franche-Comté and UBFC into a new leading institution (EPE), it unites two historical full partner institutions, UTBM and SUPMICROTECH-ENSMM, and six associated partners: Cluny Arts et Métiers campus, the Besançon University Hospital (CHU), the Bourgogne-Franche-Comté CROUS, the BFC French Blood Establishment (EFS), ESTA and ISBA. 

Marie and Louis Pasteur University aims to enhance the attractiveness and inclusivity of its programs, strengthen its academic and scientific excellence and foster innovative socio-economic partnerships, while affirming its strong regional ties and national and international influence. 

With over 30,000 students and 2,000 faculty members, the university relies on an innovative administration and reinforced synergies to address the academic, scientific, and societal challenges of today and tomorrow. 

Role in RAIDO

UMLP is particularly in charge of Pilot 4, in the “Robotics” domain, entitled “Industry 5.0 & Bio-based Composites: AI Models for Plant Fibers”. This pilot involves an experimental setup designed to perform compression tests on plant fibers using a microrobotics approach, and also to determine the geometry of each individual fiber. These experimental results are then processed through Finite Element Modeling (FEM) to determine the mechanical properties of the plant fibers. UMLP thus brings expertise in small-scale robotics and mechanical engineering.

Throughout the project, UMLP team members have conducted experimental tests and numerical simulations, resulting in both real-world and synthetic datasets that have been shared with the consortium. UMLP is also working closely with project partners to leverage these datasets for the development of a surrogate model. Further improvements to the experimental setup have been implemented to enhance automation and enable the generation of larger real-world datasets. In parallel, numerical simulations have also been carried out to generate extensive synthetic datasets.

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