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Title: An investigation into reducing the spindle acceleration energy consumption of machine tools
Authors: Lv, Jingxiang
Tang, Renzhong
Tang, Wangchujun
Liu, Ying
Zhang, Yingfeng
Jia, Shun
Keywords: Spindle acceleration
Energy consumption
Machine tools
Energy saving
Issue Date: 2017
Publisher: Elsevier / © The Authors
Citation: LV, J. ... et al, 2017. An investigation into reducing the spindle acceleration energy consumption of machine tools. Journal of Cleaner Production, 143, pp. 794 - 803.
Abstract: Machine tools are widely used in the manufacturing industry, and consume large amount of energy. Spindle acceleration appears frequently while machine tools are working. It produces power peak which is highly energy intensive. As a result, a considerable amount of energy is consumed by this acceleration during the use phase of machine tools. However, there is still a lack of understanding of the energy consumption of spindle acceleration. Therefore, this research aims to model the spindle acceleration energy consumption of computer numerical control (CNC) lathes, and to investigate potential approaches to reduce this part of consumption. The proposed model is based on the principle of spindle motor control and includes the calculation of moment of inertia for spindle drive system. Experiments are carried out based on a CNC lathe to validate the proposed model. The approaches for reducing the spindle acceleration energy consumption were developed. On the machine level, the approaches include avoiding unnecessary stopping and restarting of the spindle, shortening the acceleration time, lightweight design, proper use and maintenance of the spindle. On the system level, a machine tool selection criterion is developed for energy saving. Results show that the energy can be reduced by 10.6% to more than 50% using these approaches, most of which are practical and easy to implement.
Description: This is an Open Access article published by Elsevier and distributed under the terms of the Creative Commons Attribution Licence, CC BY 4.0, https://creativecommons.org/licenses/by/4.0/
Sponsor: This work was supported by the National Natural Science Foundation of China (Grant No.51175464) and National High Technology Research and Development Program of China (863 program, grant number 2013AA0413040). The authors acknowledge support from the EPSRC Centre for Innovative Manufacturing in Intelligent Automation, in undertaking this research work under grant reference number EP/IO33467/1.
Version: Published
DOI: 10.1016/j.jclepro.2016.12.045
URI: https://dspace.lboro.ac.uk/2134/24306
Publisher Link: http://dx.doi.org/10.1016/j.jclepro.2016.12.045
ISSN: 0959-6526
Appears in Collections:Published Articles (Mechanical, Electrical and Manufacturing Engineering)

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