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  •   University of Thessaly Institutional Repository
  • Επιστημονικές Δημοσιεύσεις Μελών ΠΘ (ΕΔΠΘ)
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  •   University of Thessaly Institutional Repository
  • Επιστημονικές Δημοσιεύσεις Μελών ΠΘ (ΕΔΠΘ)
  • Δημοσιεύσεις σε περιοδικά, συνέδρια, κεφάλαια βιβλίων κλπ.
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The effects of FFF 3D printing parameters on energy consumption

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Author
Vidakis N., Kechagias J.D., Petousis M., Vakouftsi F., Mountakis N.
Date
2022
Language
en
DOI
10.1080/10426914.2022.2105882
Keyword
Additives
Deposition
Frequency division multiplexing
Fused Deposition Modeling
Regression analysis
Statistical mechanics
3-D printing
3d
3D-printing
Consumption
Energy
Energy-consumption
Fused filament fabrication
Green
Parameter
Power
Energy utilization
Taylor and Francis Ltd.
Metadata display
Abstract
Energy metrics in 3D-printing technologies are of major importance for sustainable manufacturing. The printing parameters (PP) affect the energy consumption remarkably and define the overall Energy Printing Consumption (EPC), the Specific Printing Energy (SPE), and Specific Printing Power (SPP). This study investigates the impact of six PPs: the infill raster density (ID), raster deposition angle (RDA), nozzle temperature (NT), fused filament printing speed (PS), layer deposition thickness (LT), and bed temperature (BT), on the above three energy indexes, i.e. the EPC, SPE, and SPP. The experimental course follows the Taguchi L25 orthogonal array (OA) with six repetitions. The experimental data were dissolved and studied utilizing descriptive statistical tools, i.e. main effect plots (MEP plots), ANOVA, and response surface methodology (RSM). Six evaluation experiments validated the extracted quadratic regression models (QRM). The above statistical models thoroughly describe the effects of six intended fused filament fabrication (FFF) printing parameters on the energy metrics (EPC, SPE, and SPP). Additionally, the physical mechanism is explained in depth by applying mechanical strength evaluation tests for all ranges of parameters’ values. This study proposes a methodology for efficient and sustainable FFF. © 2022 Taylor & Francis.
URI
http://hdl.handle.net/11615/80608
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