Optimizing Next-generation Manufacturing Processes

Argonne National Laboratory is a national leader in the scaleup and commercialization of complex materials through a scientific approach to process innovation. In manufacturing R&D, process efficiency, safety, cost, and product quality are related to process configuration variables in a complex manner. Argonne researchers leverage an AI technique called Bayesian optimization, in concert with targeted experiments and simulations, to find configurations with the best performance in processes such as atomic layer deposition, flame spray synthesis, induction pipe bending, and melt blowing. This allows us to quickly develop new materials critical to next-generation chemical catalysis, solar energy, battery technology and more, all of which support a secure energy future.