Andrew Armstrong

Papers

Total Citations

143

H-Index

About

Dr. Andrew Armstrong is a leading researcher at the intersection of advanced manufacturing and machine learning, whose work is redefining how materials are discovered and optimized for 3D printing. His primary research areas include autonomous additive manufacturing, Bayesian optimization, and data-driven materials science. Dr. Armstrong’s most impactful contribution is his pioneering 2021 paper, “Toward autonomous additive manufacturing: Bayesian optimization on a 3D printer,” which has garnered 143 citations. In this work, he demonstrated how machine learning algorithms can autonomously learn optimal print parameters, dramatically accelerating materials development and reducing the labor-intensive trial-and-error traditionally required for 3D printing. This breakthrough has opened the door to self-optimizing printers capable of fabricating high-quality parts from novel materials with minimal human intervention. By bridging the gap between computational optimization and physical fabrication, Dr. Armstrong is helping to usher in a new era of intelligent, adaptive manufacturing. His research is essential reading for anyone interested in the future of automated materials discovery and the convergence of AI with real-world production systems.