2022
DOI: 10.3390/su14073757
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A New Method of Predicting the Energy Consumption of Additive Manufacturing considering the Component Working State

Abstract: With the increase in environmental awareness, coupled with an emphasis on environmental policy, achieving sustainable manufacturing is increasingly important. Additive manufacturing (AM) is an attractive technology for achieving sustainable manufacturing. However, with the diversity of AM types and various working states of machines’ components, a general method to forecast the energy consumption of AM is lacking. This paper proposes a new model considering the power of each component, the time of each process… Show more

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Cited by 6 publications
(3 citation statements)
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References 23 publications
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“…• Espach et al [38] optimizes FFF process to increase tensile from 45 to 55 MPa and reduce material consumption from 34 to 29 g. • Yan et al [39] provides a methodology for estimating SEC in AM with the parsing of g-code to provide real-time estimates of energy consumption.…”
Section: Characterization Of Energy Consumptionmentioning
confidence: 99%
“…• Espach et al [38] optimizes FFF process to increase tensile from 45 to 55 MPa and reduce material consumption from 34 to 29 g. • Yan et al [39] provides a methodology for estimating SEC in AM with the parsing of g-code to provide real-time estimates of energy consumption.…”
Section: Characterization Of Energy Consumptionmentioning
confidence: 99%
“…As research and industry focus on sustainable manufacturing increases, energy consumption in additive manufacturing processes is becoming an intriguing topic in the research community. In this sense, several studies have focused on modeling and optimizing the amount of energy consumed in additive manufacturing [18][19][20][32][33][34][35]]. In the literature, studies of energy consumption have been carried out either using specific methods or based on machine learning techniques.…”
Section: Related Workmentioning
confidence: 99%
“…The authors identify the key factors influencing the energy and material efficiency of this type of manufacturing. Zhiqiang Yan et al [34] proposed a model to predict power consumption and printing time as a function of process parameters and machine component operating states. This model takes into account "the power of each component" and "the duration of each process"; however, this model has some limitations.…”
Section: Related Workmentioning
confidence: 99%