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Title: Minimising the machining energy consumption of a machine tool by sequencing the features of a part
Authors: Hu, Luoke
Peng, Chen
Evans, Steve
Peng, Tao
Liu, Ying
Tang, Renzhong
Tiwari, Ashutosh
Keywords: Machining energy
Machine tools
Feature sequencing
Cutting volume
Depth-First Search
Genetic Algorithm
Issue Date: 2017
Publisher: Elsevier / © The Authors
Citation: HU, L. ... et al, 2017. Minimising the machining energy consumption of a machine tool by sequencing the features of a part. Energy, 121, pp. 292 - 305.
Abstract: Increasing energy price and emission reduction requirements are new challenges faced by modern manufacturers. A considerable amount of their energy consumption is attributed to the machining energy consumption of machine tools (MTE), including cutting and non-cutting energy consumption (CE and NCE). The value of MTE is affected by the processing sequence of the features within a specific part because both the cutting and non-cutting plans vary based on different feature sequences. This article aims to understand and characterise the MTE while machining a part. A CE model is developed to bridge the knowledge gap, and two sub-models for specific energy consumption and actual cutting volume are developed. Then, a single objective optimisation problem, minimising the MTE, is introduced. Two optimisation approaches, Depth-First Search (DFS) and Genetic Algorithm (GA), are employed to generate the optimal processing sequence. A case study is conducted, where five parts with 11–15 features are processed on a machining centre. By comparing the experiment results of the two algorithms, GA is recommended for the MTE model. The accuracy of our model achieved 96.25%. 14.13% and 14.00% MTE can be saved using DFS and GA, respectively. Moreover, the case study demonstrated a 20.69% machining time reduction.
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: The authors would like to thank the support from the National Natural Science Foundation of China (No. U1501248), the China Scholarship Council (No. 201406320033), the EPSRC Centre for Innovative Manufacturing in Intelligent Automation (No. EP/ IO33467/1) and the EPSRC EXHUME Project (Efficient X-sector use of Heterogeneous Materials in Manufacturing) (No. EP/K026348/1).
Version: Published
DOI: 10.1016/j.energy.2017.01.039
URI: https://dspace.lboro.ac.uk/2134/24308
Publisher Link: http://dx.doi.org/10.1016/j.energy.2017.01.039
ISSN: 0360-5442
Appears in Collections:Published Articles (Mechanical, Electrical and Manufacturing Engineering)

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