Julien Favre

Recristallisation du superalliage base cobalt L-605 pendant la déformation à chaud: L’alliage L-605 est un superalliage base cobalt combinant une haute résistance et une bonne ductilité, de plus il est biocompatible et présente une bonne résistance a la corrosion. Dû a son inertie chimique dans le corps humain, ce matériau a été utilise avec succès pour fabriquer des valves cardiaques et des stents. Le contrôle de la microstructure peut influencer grandement les propriétés mécaniques : notamment un raffinement des grains est susceptible d’augmenter d’avantage la résistance et serait intéressant pour permettre de fabriquer des stents selon une architecture plus fine. L’ajustement de la distribution de taille de grains à travers le phénomène de recristallisation lors de la déformation à chaud apparait comme une solution pratique pour ajuster les propriétés mécaniques du matériau. Pour contrôler la microstructure et choisir les conditions de procédé optimales, les mécanismes mis en jeu lors de la recristallisation dynamique et l’effet des conditions de déformation sur la taille de grain doivent être compris et prévisibles par des outils théorique. Les propriétés mécaniques du matériau à haute température sont déterminées par des essais de compression à chaud. L’évolution microstructurale du matériau lors de la compression est analysée par microscopie optique et électronique (EBSD, TEM). Le phénomène de recristallisation dynamique continue est mis en évidence, et procède par nucléation de nouveaux grains aux joints de grain. La corrélation entre le comportement mécanique à chaud et l’évolution microstructurale est déterminée expérimentalement. Les conditions optimales de déformation impliquant la recristallisation dynamique sont déterminées, et la microstructure résultante est étudiée en détail. De nouveaux outils théoriques permettant de prévoir les conditions de recristallisation et d’extraire les paramètres physiques du matériau a partir des données expérimentales sont proposés. Enfin, la recristallisation dynamique est modélisée analytiquement, et permet de prédire le comportement mécanique et l’évolution de la taille de grain lors de la déformation.

Recent developments in metallic additive manufacturing (AM) processes for the production of high-performance industrial pieces have been hampered by the limited availability of reliably processable or printable alloys. To date, most of the alloys used in AM are commercial grades that have been previously optimized for different manufacturing techniques. This study aims to design new alloys specifically tailored for AM processes, to minimize defects in the final products and to optimize their properties. A computational approach is proposed to design novel and optimized austenitic alloy compositions. This method integrates a suite of predictive tools, including machine learning, calculation of phase diagrams (CALPHAD) and physical models, all piloted by a multi-objective genetic algorithm. Within this framework, several material-dependent criteria are examined and their impact on properties and on the occurrence of defects is identified. To validate our approach, experimental tests are performed on a selected alloy composition: powder is produced by gas atomization and samples are fabricated by laser powder bed fusion. The microstructure and mechanical properties of the alloys are evaluated and its printability is compared with a commercial 316L stainless steel taken as a reference. The optimized alloy performs similarly to 316L in terms of coefficient of thermal expansion, hardness and elongation, but has a 17% lower yield strength and ultimate tensile strength (UTS), indicating that further optimization is required.

Recent developments in metallic additive manufacturing (AM) processes for the production of high-performance industrial pieces have been hampered by the limited availability of reliably processable or printable alloys. To date, most of the alloys used in AM are commercial grades that have been previously optimized for different manufacturing techniques. This study aims to design new alloys specifically tailored for AM processes, to minimize defects in the final products and optimize their properties. A computational approach is proposed to design novel and optimized austenitic alloy compositions. This method integrates a suite of predictive tools, including machine learning, calculation of phase diagrams (CALPHAD) and physical models, all piloted by a multi-objective genetic algorithm. Within this framework, several material-dependent criteria are examined and their impact on properties and on the occurrence of defects is identified. To validate our approach, experimental tests are performed on a selected alloy composition: powder is produced by gas atomization and samples are fabricated by laser powder bed fusion. The microstructure and mechanical properties of the alloys are evaluated and its printability is compared to a commercial 316L stainless steel taken as a reference.

The continuous design of cubic lattice architecture materials provides a wide range of mechanical properties. It makes possible to control the stress magnitude and the local maxima in the structure. This study reveals some architectures specifically designed to reach a good compromise between mass reduction and mechanical strength. Decreased local stress concentration prevents the early occurrence of localized plasticity or damage, and promotes the fatigue resistance. The high performance of cubic architectures is reported extensively, and structures with the best damage resistance are identified. The fatigue resistance and S–N curves (stress magnitude versus lifetime curves) can be estimated successfully, based on the investigation of the stress concentration. The output data are represented in two-dimensional (2D) color maps to help mechanical engineers in selecting the suitable architecture with the desired stress concentration factor, and eventually with the correct fatigue lifetime.