A PDE Framework for Sensing and Control of Metal Additive Manufacturing

University Of California-San Diego, La Jolla CA

Investigators

Abstract

This award will allow the development of new analytical frameworks to improve the control of metal additive manufacturing processes. Additive manufacturing technologies have revolutionized design and manufacturing, allowing rapid customization of production and product development by creating three-dimensional objects from digital models. Despite substantial efforts over the past two decades, manufacturing flaws such as geometric inaccuracy, cracks, and micro-structural inhomogeneity often occur during the build. These issues primarily arise from a variety of factors, including the interaction between the laser beam and powder bed, that affect the thermal history during the manufacturing process. This research aims to improve the manufacturing of functional metallic final products through theoretical and experimental investigations that will enable better regulation and control of the temperature cooling rate and the width of the melt-pool, both are key factors in printing complex geometries. The successful completion of this project will positively impact the use of metal additive manufacturing for fabrication of functional parts in high-value industries such as aerospace and defense. Through a thorough outreach and education plan, undergraduate and graduate students will participate in interdisciplinary learning activities that couple advanced manufacturing with system theory. Existing control approaches for metal additive manufacturing processes such as selective laser melting are built on approximate ordinary differential equation (ODE) models or lumped parameter assumptions. These simplified approaches do not precisely capture the spatially distributed nature of the temperature field and completely ignore phase change dynamics, the effect of layer geometry, as well as prior laser passes. Conversely, predictive partial differential equation (PDE) models with phase change dynamics are too complex for real-time control. Through this research effort, we offer control-oriented PDE-ODE models derived from fundamental conservation laws that incorporate phase-change dynamics and enable real-time estimation and control of process signatures. Specifically, this project aims to create and experimentally validate a PDE-ODE model-based approach for the design of estimators that rely on melt-front measurements alone to estimate temperature profiles and feedback controllers that use these estimates to robustly regulate melt pool size and cooling rates for varying layer geometry and local thermal properties. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.