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A particle swarm optimisation-based Grey prediction model for thermal error compensation on CNC machine tools

Abdulshahed, Ali, Longstaff, Andrew P. and Fletcher, Simon (2015) A particle swarm optimisation-based Grey prediction model for thermal error compensation on CNC machine tools. In: Laser Metrology and Machine Performance XI, LAMDAMAP 2015. Euspen, Huddersfield, UK, pp. 369-378. ISBN 978-0-9566790-5-5

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Abstract

Thermal errors can have a significant effect on CNC machine tool accuracy. The thermal error compensation system has become a cost-effective method of improving machine tool accuracy in recent years. In the presented paper, the Grey relational analysis (GRA) was employed to obtain the similarity degrees between fixed temperature sensors and the thermal response of the CNC machine tool structure. Subsequently, a new Grey model with convolution integral GMC(1, N) is used to design a thermal prediction model. To improve the accuracy of the proposed model, the generation coefficients of GMC(1, N) are calibrated using an adaptive Particle Swarm Optimisation (PSO) algorithm. The results demonstrate good agreement between the experimental and predicted thermal error. Finally, the capabilities and the limitations of the model for thermal error compensation have been discussed.
Keywords: CNC machine tool, Thermal error modelling, ANFIS, Fuzzy logic, Grey system theory.

Item Type: Book Chapter
Subjects: T Technology > TJ Mechanical engineering and machinery
T Technology > TS Manufactures
Schools: School of Computing and Engineering
School of Computing and Engineering > Centre for Precision Technologies
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Depositing User: Ali Abdulshahed
Date Deposited: 19 Mar 2015 14:17
Last Modified: 06 Nov 2015 16:28
URI: http://eprints.hud.ac.uk/id/eprint/23828

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