Energy consumption distribution and optimization of additive manufacturing

被引:0
|
作者
Zhilin Ma
Mengdi Gao
Qingyang Wang
Nan Wang
Lei Li
Conghu Liu
Zhifeng Liu
机构
[1] Suzhou University,School of Mechanical and Electronic Engineering
[2] Hefei University of Technology,School of Mechanical Engineering
[3] Tsinghua University,Tsinghua University School of Economics and Management
关键词
Additive manufacturing; Energy consumption distribution; Energy units; Parameter optimization;
D O I
暂无
中图分类号
学科分类号
摘要
With growing concerns about energy and environmental issues, much research attention has been focused on manufacturing activities that consume significant amounts of energy and influence the environment. In the manufacturing field, additive manufacturing (AM) is a new production technology that can process complex parts and has high material utilization, which also has the problem of excessive energy consumption and has raised concern. However, the existing research primarily focuses on the process of AM energy consumption and its impact on the environment; the energy consumption distribution of AM equipment is still lacking. This study proposes an analytical method for addressing the energy consumption distribution of AM equipment by classifying the equipment into different energy units. In particular, the energy consumption and energy distribution of different types of AM equipment including fused deposition modeling (FDM), stereo lithography apparatus, and selective laser melting are discussed. Then, the energy consumption distribution characteristics of the three different AM equipment are investigated by machining a conventional structure using the proposed energy consumption quantification method based on energy units. The results show that the proposed method can effectively and quickly predict the energy consumption of AM equipment. Based on the energy consumption distribution method, to improve the process energy efficiency, a process optimization method considering energy consumption and forming quality is proposed to obtain the optimal process parameters of FDM. This method can provide support for energy consumption prediction and energy efficiency improvement of AM.
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页码:3377 / 3390
页数:13
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