Doubling energy efficiency, including the efficiency of electrical engines, was put forward in COP28 as a critical mandate to restrict the rise in the
average temperature of the Earth by 1.5K, as it would enable a wider decarbonization of transport. The development of technologies aimed at reducing losses in electric motors is urgent. The main sources of losses in electric motors, of electrical and magnetic origin, stem from the limited coercivity of the silicon steel that is currently used to fabricate components such as rotors and stators, and from the fact that conventional manufacturing methods cannot produce parts with the complex geometry that would be ideal to guide magnetic field in an optimum way. 

Fe-based metallic glasses (MG) stand out for their exceptional soft-magnetic properties, that outperform drastically those of silicon steel. The primary obstacle to the broader commercialization of Febased MGs is their high propensity to crystallize during cooling, which surpasses that of other alloy families, such as for example Zr-based MGs. Powder -based additive manufacturing methods, such as laser powder bed fusion (LPBF), have recently emerged as promising avenues for fabricating large MG parts. LPBF consists of the consolidation of consecutive layers of metallic powders by a laser source following a pre-defined path. This process involves very high local cooling rates which are comparable to those required for stabilizing the amorphous phase in most MG alloys. Fe-based MG parts produced via LPBF to date, including those with simple geometries, feature microstructures comprising glassy-crystalline composites, as crystallization inevitably occurs during solidification and the subsequent thermal cycles caused by the deposition of adjacent tracks or layers. Moreover, the LPBFmanufactured specimens are still populated with cracks that must be eliminated before these materials can be widely commercialized. Optimizing density and amorphous fraction simultaneously while eliminating cracks remains a challenge to date in Fe-based MG parts, even in those with simple geometry, because LPBF is a complex process that involves more than 100 variables. 

Key parameters that must be simultaneously optimized include the laser power, the scan speed, the layer thickness, the hatch distance, the laser cross-section, and the scanning strategy. This task exceeds the capability of human experimentalists and must be aided by digital tools, which do not exist to date. This project aims to develop a digital tool to process dense, fully amorphous, and crack-free Fe-based MG parts with complex geometry, such as that of the rotors and stators in electric motors, via LPBF. The tool, which will include computational models based on physics (thermo-mechanical-metallurgical approach) and artificial intelligence to predict the effects of the key processing parameters, must be informed by processing-structure-magnetic/mechanical property relationships that will be obtained experimentally within a limited processing space in simple geometry specimens. The tool will first be developed for Zr-based MGs which, because of their large glass-forming ability, will facilitate the experimental derivation of the processing-structure-relationships that are needed to develop accurate predictive models. At a later stage of the project, the digital tool will be utilized for LPBF process optimization of novel soft magnetic Fe-based MG alloys.