Agency: Agence National de la Recherche (ANR)
Instrument: Appel à Projet (AAP) 2023
ID: ANR-24-CE08-3737
Institutions: University of Lorraine, Ecole Polytechnique and University of Grenoble-Alpes
Co-PIs: Vincent Taupin, Manas Upadhyay, Marc Fivel
Core scientific team: Andreas Ntinos (Ph.D. student), Guilhem Martin (Professor, University of Grenoble-Alpes), Antoine Guitton (Associate Professor, University of Lorraine)
Dates: October 2024 – August 2028
Funds: 646,976 €
Webpage: https://anr.fr/Project-ANR-24-CE08-3737
Aim: To enhance the performance and reliability of additively manufactured (AM) Cu-Cr alloys by combining experiments, multiscale modelling, and machine learning (ML) to establish quantitative links between processing conditions, microstructure evolution, and mechanical properties.
Objectives:
- Investigate Cu-Cr alloys as a model AM system to study process–microstructure–property relationships.
- Generate reliable thermomechanical data for Cr, addressing a longstanding gap in the literature that hinders accurate modelling and alloy design.
- Develop and validate predictive models for computational fluid dynamics, solidification, phase evolution, and residual stress formation in Cu-Cr alloys under AM conditions.
- Apply ML-assisted active learning to guide experiments and optimize AM process parameters for targeted mechanical performance.
Methods:
- AM of dense Cu–Cr alloys via LPBF.
- Characterization via SEM, TEM, and diffraction methods to capture grain structure, segregation, and phase distributions; mechanical testing to correlate microstructure with properties
- Thermomechanical modelling using finite element, crystal plasticity, and cellular automaton frameworks, informed by the new Cr datasets to complement experiments
- ML pipelines for process optimization, leveraging active learning to focus experiments where predictive uncertainty is high.
Expected Impact and Output:
- First systematic datasets on the thermomechanical response of Cr at multiple temperatures. Filling a huge gap in the literature for this important alloying element.
- Validated framework linking AM parameters, Cu–Cr microstructures, and mechanical performance, transferrable to other alloy systems.
- Optimized lasering strategies that enhance strength, fatigue, and wear resistance of Cu–Cr alloys in direct connection with project LaserSurf.
- Publications, conference talks, and foundations for industrial partnerships on AM of Cu-Cr alloys.