Abstract:

We provide an overview of a recent NASA University Leadership Initiative project entitled “Development of an Ecosystem for Qualification of Additive Manufacturing Processes and Materials in Aviation”. This work demonstrated the dependence of fatigue on defect structures in AM Ti-6Al-4V. Our current NASA Science & Technology Research Institute “Institute for Model-Based Qualification & Certification of Additive Manufacturing (IMQCAM)” is creating a numerical digital twin (DT) for predicting fatigue as a function of process parameters. The key result of a systematic variation across power-velocity space was that the defect number density was anti-correlated with fatigue life. The fatigue-based process window was much narrower compared to typical reports and explainable in terms of observed variations based on spatter rates and intermittent lack of melt pool overlap. We report on recent developments in modeling microstructure, texture and pores along with variations in heat treatment. We conclude that establishing a qualified materials process is feasible via an efficient survey of a limited domain of process space. Linking models together in the DT requires substantial effort in terms of data exchange. In terms of predictability of fatigue, the limited data suggests that the scatter in life is smallest in the long crack (Paris Law) regime, intermediate for short cracks and largest for crack nucleation. This provides an important background for Uncertainty Quantification (UQ), which is a major focus for the IMQCAM team and which has already revealed a significant sensitivity to compositional variations, for example.

Support from multiple agencies is gratefully acknowledged, including NASA, DOE/BES, DOE/NNSA, ONR, NSF, OEA, Commonwealth of Pennsylvania, and Ametek. I am indebted to my many collaborators.

Bio:

I have been a member of the faculty at Carnegie Mellon University since 1995. I am also the Co-Director of the NextManufacturing Center on additive manufacturing. Previously, I worked for the University of California at the Los Alamos National Laboratory. I spent nine years in management with four years as a Group Leader (and then Deputy Division Director) at Los Alamos, followed by five years as Department Head at CMU (up to 2000). I have been a Fellow of ASM since 1996, Fellow of the Institute of Physics (UK) since 2004 and Fellow of TMS since 2011. I received the Cyril Stanley Smith Award from TMS in 2014, was elected as Member of Honor by the French Metallurgical Society in 2015 and then became the US Steel Professor of Metallurgical Engineering and Materials Science in 2017. I received Cyril Stanley Smith Award from the International Conference on Recrystallization and Grain Growth in 2019 and the Hans Bunge Award from the Intl. Conf. on Textures of Materials (ICOTOM) in 2024. I was an International Francqui Professor (Belgium) in 2022 and I received the ASM Gold Medal and was promoted to University Professor, both in 2024. My research focuses on processing-microstructure-properties relationships with interests in additive manufacturing, the measurement and prediction of microstructural evolution, the relationship between microstructure and properties, especially three-dimensional effects, texture & anisotropy and the use of synchrotron x-rays.

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.

Abstract

Programmable materials and structures hold great potential for various applications, such as robotics, biomedical devices, and civil structures. The rational design, physical realization, and validation of programmed behaviors in these systems play important roles in enabling functional devices. To encode desired mechanical functionality into structures, we propose a multi-material multi-objective topology optimization approach to inverse design composite structures that achieve complex target mechanical responses under large deformations. The multi-material framework simultaneously optimizes both the geometry, material heterogeneity, and architecture to achieve target behaviors and functionalities. A library of diverse designs is created, showcasing a wide range of precisely programmed nonlinear responses, such as multi-bulking and multi-plateau. In general, the properties of materials and structures typically remain fixed after being constructed. To enable reprogrammable behaviors, we develop a multi-physics topology optimization approach to discover magneto-active and temperature-active materials that achieve tunable buckling and switchable shape morphing, controlled by magnetic fields and temperature fields, respectively. The obtained systems exhibit one response under one stimulus and switch to a distinct response by applying another stimulus. To bridge the gap between simulation and fabrication, we explore multi-material manufacturing techniques, introduce advanced path generation methods, and develop direct ink writing (DIW) techniques to fabricate a suite of mechanical, magnetic, and thermal metamaterials and metastructures and experimentally validate their programmed behaviors. The excellent agreement among target, simulation, and experiment demonstrates that the proposed optimization-driven framework, when integrated with hybrid manufacturing techniques, has the potential to systematically design, inform, and create innovative multi-functional materials and structures for various engineering applications.

Bio

Dr. Xiaojia Shelly Zhang is a David C. Crawford Faculty Scholar and Associate Professor at the Department of Civil and Environmental Engineering and the Department of Mechanical Science and Engineering at the University of Illinois at Urbana Champaign (UIUC). She directs the MISSION (MuIti-functional Structures and Systems desIgn OptimizatioN) Laboratory. Dr. Zhang holds B.S. and M.S. degrees from UIUC and a Ph.D. degree from Georgia Tech. Her research explores multi-physics topology optimization, inverse design, stochastic learning algorithms, and additive manufacturing to develop multi-functional, sustainable, and resilient materials, structures, and robots for applications at different scales. She is the recipient of the National Science Foundation CAREER Award (2021), the ASME Journal of Applied Mechanics Award (2022), the DARPA Young Faculty Award (2022), the AFOSR Young Investigator Award (2023), the Leonardo da Vinci Award from ASCE (2024), the DARPA Director's Fellowship (2024), UIUC Campus Distinguished Promotion Award (2025), the Thomas J.R. Hughes Young Investigator Award from ASME (2025), the ASME Henry Hess Early Career Publication Award (2025), the Haftka Young Investigator Award from International Society for Structural and Multidisciplinary Optimization (2025), and Huajian Gao Young Investigator Medal from SES (2026). Dr. Zhang serves on the Executive Committee of the International Society of Structural and Multidisciplinary Optimization (ISSMO) and is a Review Editor for the Journal of Structural and Multidisciplinary Optimization and an Associate Editor for the Journal of Applied Mechanics.