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Comparative study of ANN and ANFIS prediction models for thermal error compensation on CNC machine tools

Abdulshahed, Ali, Longstaff, Andrew P., Fletcher, Simon and Myers, Alan (2013) Comparative study of ANN and ANFIS prediction models for thermal error compensation on CNC machine tools. In: Laser Metrology and Machine Performance X. LAMDAMAP 2013 . EUSPEN, Buckinghamshire, UK, pp. 79-89. ISBN 978-0-9566790-1-7

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Thermal errors can have significant effects on CNC machine tool accuracy. The errors usually come from thermal deformations of the machine elements created by heat sources within the machine structure or from ambient change. The performance of a thermal error compensation system inherently depends on the accuracy and robustness of the thermal error model. In this paper, Adaptive Neuro Fuzzy Inference System (ANFIS), Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) techniques were employed to design four thermal prediction models: ANFIS by dividing the data space into rule patches (ANFIS-Scatter partition model); ANFIS by dividing the data space into rectangular sub-spaces (ANFIS-Grid partition model); ANN with a back-propagation algorithm (ANN-BP model) and ANN with a PSO algorithm (ANN-PSO model). Grey system theory was also used to obtain the influence ranking of the input sensors on the thermal drift of the machine structure. Four different models were designed, based on the higher-ranked sensors on thermal drift of the spindle. According to the results, the ANFIS models are superior in terms of the accuracy of their predictive ability; the results also show ANN-BP to have a relatively good level of accuracy. In all the models used in this study, the accuracy of the results produced by the ANFIS models was higher than that produced by the ANN models.

Item Type: Book Chapter
Subjects: T Technology > TS Manufactures
Schools: School of Computing and Engineering
School of Computing and Engineering > Centre for Precision Technologies > Engineering Control and Machine Performance Research Group
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Depositing User: Ali Abdulshahed
Date Deposited: 26 Mar 2013 16:32
Last Modified: 24 Mar 2016 12:01


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