Attend a seminar on generalizable geometric patterns in additive manufacturing, Oct. 2

About

Hannah Budinoff is an assistant professor of systems and industrial engineering at the University of Arizona. She holds doctoral and master’s degrees in mechanical engineering from the University of California, Berkeley, and a bachelor’s degree in mechanical engineering from the University of Arizona. Her research interests include automated geometric manufacturability analysis with a focus on additive manufacturing processes, the development of design support tools and design for sustainability. Budinoff actively collaborates with government, academic and industry researchers on projects supported by funding from sources including the National Science Foundation, National Institute of Standards and Technology and U.S. Department of Defense agencies.

Abstract

Additive manufacturing enables the fabrication of geometrically complex designs, but manufacturability depends strongly on part geometry. High-fidelity, physics-based simulations can reveal relationships between manufacturing outcomes and geometric features, but these simulations can be computationally expensive and too context-specific to directly inform early design decisions.

In this talk, Budinoff will describe her group’s ongoing efforts to identify generalizable relationships between part geometry and additive manufacturing outcomes that can be translated into actionable design guidance. She will introduce several studies focused on laser powder bed fusion, or LPBF, in which her group uses process simulations to predict outcomes including recoater collision, part distortion, build time and cost. She will describe how the researchers combine feature engineering, computational geometry, clustering and machine learning to identify relationships between part geometry and manufacturing outcomes.

Budinoff will also discuss how the choice of shape representation and associated machine learning architecture influences trade-offs among predictive accuracy, computational expense and interpretability, with examples involving prismatic and topologically optimized parts. Ultimately, her research seeks to identify generalizable geometric relationships that drive manufacturing outcomes, helping transition from expensive, repeated simulations to efficient geometric evaluations. These geometry-focused evaluations can be incorporated into computer-aided design packages, process planning software and generative design approaches, enabling faster and more effective design for additive manufacturing.

From Simulations to Design Guidance: Learning Generalizable Geometric Patterns in Additive Manufacturing seminar
Friday, Oct. 2, 2026
10:30 a.m.–noon
Interdisciplinary Science and Technology Building 12 (ITSB 12) room 215, Polytechnic campus [map]