Abstract

Future aerospace platforms, from aerial systems and spacecraft, need to meet rising demands for acoustic and electromagnetic interference (EMI) mitigation, operation in harsh environments, and payload maximization, all while reducing mass. In this talk, we will discuss our work addressing some of these challenges by uniting biological design principles with machine intelligence to create and reliably fabricate multifunctional structures. First, we will present our work on bioinspired aeroacoustic designs, where drawing inspiration from owl feathers and cicada insect wings, we create a three-dimensional sinusoidal-serration drone propeller that achieves quieter flight while improving propulsive efficiency relative to an industry benchmark. Second, we will share how electromagnetic response emerges from structure, as mechanical metamaterial lattices and chameleon-inspired reconfigurable architectures enable lightweight, load-bearing EMI shielding with switchable absorption and transmission. Finally, we will connect these multifunctional designs to smart additive manufacturing where computer vision and deep learning can detect and correct fused filament fabrication anomalies in real-time. We will discuss how vision transformer-based models can ensure process robustness even under noisy, limited-data conditions, and how we quantify environmental effects, such as humidity and vibration, to establish a pathway toward adaptive manufacturing in real-world environments. This work illustrates a new paradigm where geometry, intelligence, and manufacturing co-evolve to enable rapid development of next-generation multifunctional aerospace systems.

Bio

Dr. Grace X. Gu is an Associate Professor of Mechanical Engineering at the University of California, Berkeley and currently holds the Don M. Cunningham Endowed Professorship. She received her PhD and MS in Mechanical Engineering from the Massachusetts Institute of Technology and her BS in Mechanical Engineering from the University of Michigan, Ann Arbor. The research interests of the Gu Group focus on creating new materials and devices with superior properties for mechanical, biological, and aerospace applications through computational modeling and machine learning, as well as developing AI-driven manufacturing processes to realize complex material designs. Gu is the recipient of several awards, including the Presidential Early Career Award for Scientists and Engineers (PECASE), ARO Early Career Program Award, DARPA Young Faculty Award, AFOSR Young Investigator Award, ONR Young Investigator Award, LLNL Early Career UC Faculty Initiative Award, SES Young Investigator Medal, ASME Sia Nemat-Nasser Early Career Award, MIT Technology Review 35 Innovators Under 35, and Sloan Research Fellowship. Gu has co-organized symposiums at various conferences and serves as an editor of the Journal of Intelligent Materials Systems and Structures and Composites Science and Technology. She organizes an annual 3D printing workshop at Berkeley aimed at inspiring the next generation of students to pursue careers in science and engineering.