A new method of predicting the energy consumption of additive manufacturing considering the component working state

Yan, Zhiqiang, Huang, Jian, Lv, Jingxiang, Hui, Jizhuang, Liu, Ying ORCID: https://orcid.org/0000-0001-9319-5940, Zhang, Hao, Yin, Enhuai and Liu, Qingtao ORCID: https://orcid.org/0000-0001-9319-5940 2022. A new method of predicting the energy consumption of additive manufacturing considering the component working state. Sustainability 14 (7) , 3757. 10.3390/su14073757

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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 and the working state of each component to predict the energy consumption. Fused deposition modeling, which is a typical AM process, was selected to demonstrate the effectiveness of the proposed model. It was found that the proposed model had a higher prediction accuracy compared to the specific energy model and the process-based energy consumption model. The proposed model could be easily integrated into the software to visualize the printing time and energy consumption of each process in each component, and, further, provide a reference for coordinating the optimization of parts’ quality and energy consumption

Item Type:	Article
Date Type:	Published Online
Status:	Published
Schools:	Engineering
Additional Information:	This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/)
Publisher:	MDPI
ISSN:	2071-1050
Date of First Compliant Deposit:	24 March 2022
Date of Acceptance:	21 March 2022
Last Modified:	08 May 2023 19:29
URI:	https://orca.cardiff.ac.uk/id/eprint/148593

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