IMX Talks - From Powder to Performance: Microstructure Engineering in Metal Additive Manufacturing for Advanced Applications

Metal additive manufacturing (AM) methods such as Laser Powder Bed Fusion (LPBF) have evolved from tools for producing parts with complex geometries into versatile platforms for tailoring alloy microstructures and properties.
In this talk, I will discuss approaches for microstructure engineering of Fe- and Al-based alloys via LPBF. I will show how alloy and process optimization allow us to adjust local composition, texture, and phase transformations, and how these changes influence the mechanical and functional properties of these alloys. By controlling solidification dynamics and selective evaporation of elements, it is possible to tune phase formation and defect populations in parts with complex 3D geometries. These examples illustrate how real-time process control enables the fabrication of components with adaptive and multifunctional characteristics, pointing toward the next generation of microstructure-aware metal additive manufacturing.

Bio: Christian Leinenbach leads the Advanced Processing and Additive Manufacturing of Metals group across the Dübendorf/Zürich and Thun sites. He also serves as Maître d'Enseignement et de Recherche at the Institute of Materials at EPFL, where he gives courses on advanced metallurgy, metals processing, and additive manufacturing. He earned his MSc in Materials Science and Engineering from the Universities of Saarbrücken (Germany) and Luleå (Sweden) in 2000, and his PhD from the University of Kaiserslautern (Germany) in 2004.
His research focuses on the development and characterization of high-performance structural alloys and metal–matrix composites, with an emphasis on additive manufacturing and laser-based processing. Key areas of interest include Al- and Ni-based alloys, refractory high-entropy alloys, oxide-dispersion-strengthened materials, and shape-memory alloys. His work integrates computational alloy design via high-throughput simulations and machine-learning-assisted thermodynamic modelling with advanced characterization methods such as synchrotron and neutron imaging and diffraction.