Abdulshahed, Ali, Longstaff, Andrew P., Fletcher, Simon and Myers, Alan (2015) Thermal error modelling of machine tools based on ANFIS with fuzzy c-means clustering using a thermal imaging camera. Applied Mathematical Modelling, 39 (7). pp. 1837-1852. ISSN 0307-904X
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Available under License Creative Commons Attribution Non-commercial No Derivatives.
Thermal errors are often quoted as being the largest contributor to CNC machine tool errors, but they can be effectively reduced using error compensation. The performance of a thermal error compensation system depends on the accuracy and robustness of the thermal error model and the quality of the inputs to the model. The location of temperature measurement must provide a representative measurement of the change in temperature that will affect the machine structure. The number of sensors and their locations are not always intuitive and the time required to identify the optimal locations is often prohibitive, resulting in compromise and poor results.
In this paper, a new intelligent compensation system for reducing thermal errors of machine tools using data obtained from a thermal imaging camera is introduced. Different groups of key temperature points were identified from thermal images using a novel schema based on a Grey model GM (0, N) and Fuzzy c-means (FCM) clustering method. An Adaptive Neuro-Fuzzy Inference System with Fuzzy c-means clustering (FCM-ANFIS) was employed to design the thermal prediction model. In order to optimise the approach, a parametric study was carried out by changing the number of inputs and number of membership functions to the FCM-ANFIS model, and comparing the relative robustness of the designs. According to the results, the FCM-ANFIS model with four inputs and six membership functions achieves the best performance in terms of the accuracy of its predictive ability. The residual value of the model is smaller than ± 2 μm, which represents a 95% reduction in the thermally-induced error on the machine. Finally, the proposed method is shown to compare favourably against an Artificial Neural Network (ANN) model.
|Subjects:||Q Science > QC Physics
T Technology > T Technology (General)
T Technology > TJ Mechanical engineering and machinery
|Schools:||School of Computing and Engineering
School of Computing and Engineering > Centre for Precision Technologies > Engineering Control and Machine Performance Research Group
|Depositing User:||Ali Abdulshahed|
|Date Deposited:||21 Oct 2014 14:33|
|Last Modified:||16 Dec 2016 05:29|
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