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research-article

Enabling Feedback Control for Additive Manufacturing Processes via Enriched Analytical Solutions

[+] Author and Article Information
John Steuben

Member of ASME, Computational Multiphysics Systems Laboratory, Center of Materials Physics and Technology, Naval Research Laboratory, Washington DC, 20375, USA
john.steuben@nrl.navy.mil

Andrew Birnbaum

Member of ASME, Computational Multiphysics Systems Laboratory, Center of Materials Physics and Technology, Naval Research Laboratory, Washington DC, 20375, USA
andrew.birnbaum@nrl.navy.mil

Athanasios P. Iliopoulos

Member of ASME, Computational Multiphysics Systems Laboratory, Center of Materials Physics and Technology, Naval Research Laboratory, Washington DC, 20375, USA
athanasios.iliopoulos@nrl.navy.mil

John Michopoulos

Fellow of ASME, Computational Multiphysics Systems Laboratory, Center of Materials Physics and Technology, Naval Research Laboratory, Washington DC, 20375, USA
john.michopoulos@nrl.navy.mil

1Corresponding author.

ASME doi:10.1115/1.4042105 History: Received August 31, 2018; Revised November 20, 2018

Abstract

Additive Manufacturing (AM) enables the fabrication of objects using successive additions of mass and energy. In this paper we explore the use of analytic solutions to model the thermal aspects of AM, in an effort to achieve high computational performance and enable "in the loop" use for feedback control of AM processes. It is shown that the utility of existing analytical solutions is limited due to their underlying assumption of a homogeneous semi-infinite domain. These solutions must therefore be enriched from their exact form in order to capture the relevant thermal physics associated with AM processes. Such enrichments include the handling of strong nonlinear variations in material properties, finite non-convex solution domains, behavior of heat sources very near boundaries, and mass accretion coupled to the thermal problem. The enriched analytic solution method (EASM) is shown to produce results equivalent to those of numerical methods which require six orders of magnitude greater computational effort. It is also shown that the EASM's computational performance is sufficient to enable AM process feedback control.

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