Thermal errors can have significant effects on CNC machine tool accuracy. The errors come from thermal\ndeformations of the machine elements caused by heat sources within the machine structure or from\nambient temperature change. The effect of temperature can be reduced by error avoidance or numerical\ncompensation. The performance of a thermal error compensation system essentially depends upon the\naccuracy and robustness of the thermal error model and its input measurements. This paper first reviews\ndifferent methods of designing thermal error models, before concentrating on employing an adaptive\nneuro fuzzy inference system (ANFIS) to design two thermal prediction models: ANFIS by dividing the data\nspace into rectangular sub-spaces (ANFIS-Grid model) and ANFIS by using the fuzzy c-means clustering\nmethod (ANFIS-FCM model). Grey system theory is used to obtain the influence ranking of all possible\ntemperature sensors on the thermal response of the machine structure. All the influence weightings of\nthe thermal sensors are clustered into groups using the fuzzy c-means (FCM) clustering method, the\ngroups then being further reduced by correlation analysis.\nA study of a small CNC milling machine is used to provide training data for the proposed models and\nthen to provide independent testing data sets. The results of the study show that the ANFIS-FCM model\nis superior in terms of the accuracy of its predictive ability with the benefit of fewer rules. The residual\nvalue of the proposed model is smaller than\n�±4 m. This combined methodology can provide improved\naccuracy and robustness of a thermal error compensation system.
Loading....