Automated In Operando Experimental Analysis of Phase Evolution During Additive Manufacturing of Multiple-Principal Element Alloys

Multiple principal element alloys (MPEAs) are proving uniquely valuable for high-value, safety-critical applications due to their tunable corrosion resistance and mechanical properties achieved through both composition and microstructure control. Additive manufacturing (AM) methods, such as directed energy deposition, surpass conventional processes by enabling a wider array of microstructural states in MPEAs. However, the rapid heating, cooling, and reheating inherent in AM can push microstructures into unique metastable states beyond thermodynamic equilibrium. This complexity challenges the manual discovery of optimal microstructures for superior corrosion resistance and mechanical properties. To bridge this knowledge gap, we are developing an automated system for real-time, in-operando experimental analysis of non-equilibrium microstructural evolution during laser-based AM of MPEAs. The project integrates high-throughput data processing of in situ synchrotron X-ray imaging and diffraction results with machine learning models. These models, processed by low-latency edge computing devices, will enable the automated discovery of optimal processing-microstructure relationships.