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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/26169

Title: Sequencing the features to minimise the non-cutting energy consumption in machining considering the change of spindle rotation speed
Authors: Hu, Luoke
Liu, Ying
Lohse, Niels
Tang, Renzhong
Lv, Jingxiang
Peng, Chen
Evans, Steve
Keywords: Non-cutting energy consumption
Sustainable manufacturing
Spindle rotation
Feature sequencing
Ant colony optimisation
Issue Date: 2017
Publisher: © 2017 The Authors. Published by Elsevier Ltd.
Citation: HU, L. ...et al., 2017. Sequencing the features to minimise the non-cutting energy consumption in machining considering the change of spindle rotation speed. Energy, 139, pp. 935-946.
Abstract: A considerable amount of energy consumed by machine tools is attributable to non-cutting operations, including tool path, tool change, and change of spindle rotation speed. The non-cutting energy consumption of the machine tool (NCE) is affected by the processing sequence of the features of a specific part (PFS), because the plans of non-cutting operations will vary based on the different PFS. This article aims to understand the NCE between processing a specific feature and its pre or post feature, especially the energy consumed during the speed change of the spindle rotation. Based on the developed model, a single objective optimisation problem is introduced that minimises the NCE. Then, Ant Colony Optimisation (ACO) is employed to search for the optimal PFS. A case study is developed to validate the effectiveness of the proposed approach. Two parts with 12 and 15 features are processed on a machining centre. The simulation experiment results show that the optimal or nearoptimal PFS can be found. Consequently, 8.70% and 30.42% reductions in NCE are achieved for part A and part B, respectively. Further, the performance of ACO for our specific optimisation problem is discussed and validated based on comparisons with other algorithms.
Description: This is an Open Access Article. It is published by Elsevier under the Creative Commons Attribution 4.0 Unported Licence (CC BY). Full details of this licence are available at: http://creativecommons.org/licenses/by/4.0/ Supplementary data for this article is available in the Loughborough Data Repository at doi: 10.17028/rd.lboro.5570041
Sponsor: The authors would like to thank the support from the National Natural Science Foundation of China (Grant No. U1501248), the China Scholarship Council (Grant No. 01406320033), and the EPSRC Centre for Innovative Manufacturing in Intelligent Automation (Grant No. EP/I033467/1).
Version: Published
DOI: 10.1016/j.energy.2017.08.032
URI: https://dspace.lboro.ac.uk/2134/26169
Publisher Link: https://doi.org/10.1016/j.energy.2017.08.032
Related Resource: https://doi.org/10.17028/rd.lboro.5570041
ISSN: 0360-5442
Appears in Collections:Published Articles (Mechanical, Electrical and Manufacturing Engineering)

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