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A New Method of Predicting the Energy Consumption of Additive Manufacturing considering the Component Working State

Zhiqiang Yan, Jian Huang, Jingxiang Lv, Jizhuang Hui, Ying Liu, Hao Zhang, Enhuai Yin, Qingtao Liu

2022Sustainability13 citationsDOIOpen Access PDF

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.

Topics & Concepts

Component (thermodynamics)Energy consumptionProcess (computing)Computer scienceConsumption (sociology)Fused deposition modelingQuality (philosophy)Energy (signal processing)SoftwareIndustrial engineeringReliability engineeringManufacturing engineeringProcess engineeringEngineering3D printingMechanical engineeringThermodynamicsEpistemologyProgramming languageSocial scienceOperating systemSociologyStatisticsMathematicsElectrical engineeringPhysicsPhilosophyAdditive Manufacturing and 3D Printing TechnologiesManufacturing Process and OptimizationAdditive Manufacturing Materials and Processes
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