Minimising the machining energy consumption of a machine tool by sequencing the features of a part

Hu, L., Peng, C., Evans, S., Peng, T., Liu, Y. , Tang, R. and Tiwari, A. (2017) Minimising the machining energy consumption of a machine tool by sequencing the features of a part. Energy, 121, pp. 292-305. (doi: 10.1016/j.energy.2017.01.039)

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.

• Item Type: Articles
• Additional Information: 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).
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Liu, Dr Ying
• Authors: Hu, L., Peng, C., Evans, S., Peng, T., Liu, Y., Tang, R., and Tiwari, A.
• College/School: College of Science and Engineering > School of Engineering > Systems Power and Energy
• Journal Name: Energy
• Publisher: Elsevier
• ISSN: 0360-5442
• Published Online: 11 January 2017
• Copyright Holders: Copyright © 2017 The Authors
• First Published: First published in Energy 121: 292-305
• Publisher Policy: Reproduced under a Creative Commons license