Abstract
Thermal errors can have significant effects on CNC machine tool accuracy. The errors come from thermal deformations of the machine elements caused by heat sources within the machine structure or from ambient temperature change. The effect of temperature can be reduced by error avoidance or numerical compensation. The performance of a thermal error compensation system essentially depends upon the accuracy and robustness of the thermal error model and its input measurements. This paper first reviews different methods of designing thermal error models, before concentrating on employing an adaptive neuro fuzzy inference system (ANFIS) to design two thermal prediction models: ANFIS by dividing the data space into rectangular sub-spaces (ANFIS-Grid model) and ANFIS by using the fuzzy c-means clustering method (ANFIS-FCM model). Grey system theory is used to obtain the influence ranking of all possible temperature sensors on the thermal response of the machine structure. All the influence weightings of the thermal sensors are clustered into groups using the fuzzy c-means (FCM) clustering method, the groups then being further reduced by correlation analysis.A study of a small CNC milling machine is used to provide training data for the proposed models and then to provide independent testing data sets. The results of the study show that the ANFIS-FCM model is superior in terms of the accuracy of its predictive ability with the benefit of fewer rules. The residual value of the proposed model is smaller than ±4μm. This combined methodology can provide improved accuracy and robustness of a thermal error compensation system.
Highlights
Thermal errors of machine tools, caused by internal and external heat sources, are one of the main factors affecting CNC machine tool accuracy
The results indicated that artificial neural networks (ANNs) have a good level of prediction accuracy, the adaptive neuro fuzzy inference system (ANFIS) models were superior in terms of forecasting ability
The whole block diagram of the proposed system is shown in Fig. 2, where variables T1 to TN represent the temperature data captured from the temperature sensors, and the thermal drift obtained from non-contact displacement transducers (NCDTs)
Summary
Thermal errors of machine tools, caused by internal and external heat sources, are one of the main factors affecting CNC machine tool accuracy. In the construction of the machine tool or to design symmetry and isolate heat sources [4] These are good practises to reduce the deformation of the CNC machine tool structure, they make the elimination of errors very expensive and can lead to other problems, such as increased vibration or lower acceleration. Another technique is reducing thermal errors through numerical compensation. Probing measurements can be prone to errors caused by swarf or coolant on the surface of the workpiece [3] This can be overcome by repeated measurements or other means, but incurs further cost in terms of hardware or production time. Direct thermal compensation is most applicable to fixed tooling, such as lathes [2], where a dedicated sensor can be conveniently located
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