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contributor authorKhrenov, Mikhail
contributor authorFrieden Templeton, William
contributor authorPrabha Narra, Sneha
date accessioned2025-04-21T10:25:40Z
date available2025-04-21T10:25:40Z
date copyright1/17/2025 12:00:00 AM
date issued2025
identifier issn1087-1357
identifier othermanu_147_4_041009.pdf
identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4306172
description abstractThe large temporal and spatial variations in temperature that can occur in layer-wise metal additive manufacturing (AM) lead to thermal excursions, resulting in property variations and defects. These variations cannot always be fully mitigated by simple static parameter search. To address this challenge, we propose a general approach based on modeling AM processes on the part-scale in state-space and framing AM process planning as a numerical optimal control problem. We demonstrate this approach on the problem of minimizing thermal variation in a given layer in the electron beam powder bed fusion AM process, and are able to compute globally optimal dynamic process plans. These optimized process plans are then evaluated in simulation, achieving an 87% and 86% reduction in cumulative variance compared to random spot melting and a uniform power field respectively, and are further validated in experiment. This one-shot feedforward planning approach expands the capabilities of AM technology by minimizing the need for iterative experiments and simulations to achieve process optimization. Further, this work opens the possibility for the application of optimal control theory to part-scale optimization and control in AM.
publisherThe American Society of Mechanical Engineers (ASME)
titleADDOPT: An Additive Manufacturing Optimal Control Framework Demonstrated in Minimizing Layer-Level Thermal Variance in Electron Beam Powder Bed Fusion
typeJournal Paper
journal volume147
journal issue4
journal titleJournal of Manufacturing Science and Engineering
identifier doi10.1115/1.4067325
journal fristpage41009-1
journal lastpage41009-10
page10
treeJournal of Manufacturing Science and Engineering:;2025:;volume( 147 ):;issue: 004
contenttypeFulltext


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