On the correlation between process parameters and specific energy consumption in fused deposition modelling

Lunetto, Vincenzo; Priarone, Paolo Claudio; Galati, Manuela; Minetola, Paolo

doi:10.1016/j.jmapro.2020.06.002

Cited by 39 publications

(15 citation statements)

References 22 publications

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“…The resultant regression models showed that power demand and energy consumption can be expressed as an increasing linear function of MAR and a function of MAR −1 , respectively. These results were in line with the energy behavior observed for low performance polymers and other AM techniques (Lunetto et al , 2020; Liu et al , 2018). A high MAR of FFF indicates that a large amount of filament is consumed in a short time.…”

Section: Conclusion and Discussionsupporting

confidence: 90%

“…Recently, there have been attempts to use a process rate of FFF to effectively characterize the energy consumption of FFF through process rate changes. Lunetto et al (2020) employed the concept of an average deposition rate for FFF to build regression models to estimate the specific printing energy of FFF using ABS and polycarbonate ABS. Liu et al (2018) used different AM techniques and materials to derive specific energy consumption varied by their energy densities and process rates, and they identified that the specific energy consumption decreases with the increase of a process rate of AM.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Existing studies in traditional manufacturing have widely employed a material removal rate (MRR) measure (i.e., the amount of removed material per processing time) to model energy behavior (Gutowski et al , 2009; Jeon et al , 2017; Diaz et al , 2011). The process rate of AM can serve as a holistic measure that effectively reflects complex process parameter effects on an AM machine (Lunetto et al , 2020). This supports the necessity of understanding energy dynamics based on the process rate of AM, as it effectively describes an AM status as a result of complex interactions among individual process parameter settings.…”

Section: Literature Reviewmentioning

confidence: 99%

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Characterization of power demand and energy consumption for fused filament fabrication using CFR-PEEK

Kim

Noh

Park

et al. 2022

RPJ

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Purpose This study aims to model power demand and energy consumption of fused filament fabrication (FFF) for carbon fiber-reinforced polyether-ether-ketone (CFR-PEEK) based on a material addition rate (MAR), which is affected by process parameter changes in an FFF machine. Moreover, a virtual additive manufacturing (AM) plant handling multiple FFF machines and part designs is simulated to compare the energy and production dynamics of operational strategies that treat part orders differently based on their inherent MAR. Design/methodology/approach A full-factorial design of experiments considering major FFF parameters (i.e., layer thickness and printing speed) is planned to fabricate CFR-PEEK samples for each process parameter combination. Then, the MAR of each process parameter combination is calculated to derive regression models for average power demand and total energy consumption. Furthermore, a discrete-event simulation model for a virtual AM system of aircraft parts is built to analyze changes in power demand and energy consumption along with order lead time and production volume under three operational strategies (i.e., higher MAR first-out, first-in-first-out, and lower MAR first-out). Findings The MAR of FFF for CFR-PEEK plays a key role in energy dynamics in which a decrease in energy consumption dominates over an increase in power demand as the MAR increases. Furthermore, preferentially processing parts with a higher MAR in the AM system is the most beneficial strategy in both energy consumption and productivity. Originality/value The findings from this study show that the energy performance of CFR-PEEK applications in FFF should be understood with the MAR of an AM system because the impact of AM complexity on energy performance can be operationally controlled by managing the MAR of part orders for the entire AM system.

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Section: Conclusion and Discussionsupporting

confidence: 90%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Characterization of power demand and energy consumption for fused filament fabrication using CFR-PEEK

Kim

Noh

Park

et al. 2022

RPJ

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show abstract

“…The majority of those studies, however, only perform experiments [ 11 – 17 ] that show the relationship between process parameters and performance measures in a summarized model without suggesting specific applications. For example, various regression [ 16 ], statistical [ 17 ], and mathematical [ 13 , 20 ] studies have suggested equivalent specific energy consumption (SEC) models or material addition rate (MAR) models based on AM parameters. These studies, however, do not consider useful applications that can be made based on their experiments.…”

Section: Introductionmentioning

confidence: 99%

Simulating energy consumption based on material addition rates for material extrusion of CFR-PEEK: a trade-off between energy costs and cycle time

Hassan

Noh

Park

et al. 2022

Int J Adv Manuf Technol

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While many studies for material extrusion–based additive manufacturing (AM) of polymers focus on experimental approaches to evaluate relevant performance measures from process parameters, there is a lack of discussion to connect experimental results with useful applications. Also, one of the major deficiencies in the application literature is a trade-off analysis between energy costs and cycle time (time to produce an item from the beginning to the end) since improving these two measures simultaneously is challenging. Thus, this paper proposes an energy simulation method for performing a trade-off analysis between energy costs and cycle time using combinations of major AM process parameters for material extrusion. We conduct experiments using carbon fiber–reinforced poly-ether-ether-ketone (CFR-PEEK), which is increasingly used in material extrusion. From experimental results, we build a power model in which power (kW) is derived as a linear function of material addition rates (MAR). This MAR regression model is then used in a proposed simulation model that integrates discrete event simulation and numerical simulation. In our simulation case study of 50 machines and 40 scenarios, we investigate trade-offs between energy costs and cycle time with three control policies (P 1 , P 25 , and P 50 ) that allow 1, 25, or 50 machines to start heating, respectively. The trade-off analysis results show that P 25 can be preferred when a balance between cycle time and energy costs is pursued, while P 1 or P 50 can be chosen if either energy cost (with P 1 ) or cycle time (with P 50 ) is more important than the other measure. Moreover, we find that the machine utilization, variability, and product volume have significant effects on energy costs and cycle time.

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“…However, the energy demand of the machining process is comprehensive, so the evaluation of energy consumption is very complicated and involves too many parameters related to the machining process [6] . G-code acts as a bridge between design software and hardware equipment in 3D machining processes, characterizing the energy consumption of machining equipment components [7] . By analyzing the G code before processing and the correlation between the G code and the energyconsuming components of the 3D printer, the estimation of energy consumption can be effectively simplified.…”

Section: Introductionmentioning

confidence: 99%

Prediction and Analysis of Energy Consumption in Fused Deposition Processing Based on G-code

Zhang

Zhao

Shen

2022

J. Phys.: Conf. Ser.

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In order to solve the problem of energy consumption prediction in the fused deposition process, an energy consumption prediction method is proposed by associating G-code files with fused deposition hardware. This method systematically studies the energy consumption in the process of fused deposition 3D printing by analyzing the relationship between G code files and fused deposition 3D printers. Starting from the energy consumption mechanism, a prediction model of fused deposition energy consumption for G code files is proposed. By obtaining the specific parameters of the fused deposition processing equipment and the selected experimental model, the experimental process parameter level is designed, and the orthogonal experiment is carried out to predict the energy consumption of the fusion processing in detail. The verification shows that the method can not only predict an accuracy rate of more than 90%, but also effectively predict the optimal process parameter value, which fully proves that the prediction method has high prediction accuracy and good practicability, and can be used for model processing. Provide tool support for energy consumption prediction before processing, saving production costs.

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On the correlation between process parameters and specific energy consumption in fused deposition modelling

Cited by 39 publications

References 22 publications

Characterization of power demand and energy consumption for fused filament fabrication using CFR-PEEK

Characterization of power demand and energy consumption for fused filament fabrication using CFR-PEEK

Simulating energy consumption based on material addition rates for material extrusion of CFR-PEEK: a trade-off between energy costs and cycle time

Prediction and Analysis of Energy Consumption in Fused Deposition Processing Based on G-code

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