Towards a general model to predict energy consumption for fused filament fabrication
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Department of Systems and Industrial Engineering, University of ArizonaIssue Date
2023-10-08
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Elsevier BVCitation
Manford, D., Budinoff, H. D., Callaghan, B. J., & Jeon, Y. (2023). Towards a general model to predict energy consumption for fused filament fabrication. Manufacturing Letters, 35, 1358-1365.Journal
Manufacturing LettersRights
© 2023 The Authors. Published by ELSEVIER Ltd. This is an open access article under the CC BY-NC-ND license.Collection Information
This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.Abstract
Additive manufacturing offers the opportunity to manufacture highly complex parts capable of providing improved performance, such as using lattice structures inside parts to reduce part weight. However, further research and development is required to improve the energy efficiency of the machinu85es, given the growing focus on sustainability and sustainable manufacturing. The purpose of this research is to address the interaction between part geometry, layer thickness, and printer type to minimize energy consumption for fused filament fabrication. This experiment was conducted with a Creality Ender 3, Monoprice MP Voxel, and Prusa i3 MK 3S + 3D printers using PLA filament, with two different part geometries and two settings for layer thickness. Active power during printing was recorded during warmup and printing. Energy consumption varied with printer type and layer thickness, while increased part complexity may lead to larger energy consumption. For printer selection, other factors may influence decision making of fused filament fabrication users such as quality and machine cost which have tradeoffs when compared to energy consumption. The first and second energy prediction models evaluated in this study had relatively large mean absolute error of 58 kJ and 29 kJ, when calculated using previously derived empirical coefficients and build time estimates calculated using slicing software, respectively. Our research suggests that improvements in the accuracy of energy prediction models are needed so such models can be applied to a range of printers. This research has implications for additive manufacturing service providers, makerspaces, as well as hobbyists who want to advance the sustainability of additive manufacturing.Note
Open access articleISSN
2213-8463Version
Final published versionae974a485f413a2113503eed53cd6c53
10.1016/j.mfglet.2023.08.114
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Except where otherwise noted, this item's license is described as © 2023 The Authors. Published by ELSEVIER Ltd. This is an open access article under the CC BY-NC-ND license.