Abstract
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 ±2μm, 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.
Highlights
Thermal errors can have significant effects on CNC machine tool accuracy
The fuzzy c-means (FCM)-Adaptive Neuro-Fuzzy Inference System (ANFIS) model with four inputs and six membership functions achieves the best performance in terms of the accuracy of its predictive ability
The thermal images are saved as a matrix of temperatures with a specific resolution of one pixel, which equates to over 76,000 temperature measurement points for this 320 Â 240 resolution camera
Summary
Thermal errors can have significant effects on CNC machine tool accuracy. They arise from thermal deformations of the machine elements created by external heat/cooling sources or those that exist within the structure (i.e. bearings, motors, belt drives, the flow of coolant and the environment temperature). Jan Han et al [16] proposed a correlation coefficient analysis and fuzzy c-means clustering for selecting temperature sensors both in their robust regression thermal error model and ANN model [17]; the number of thermal sensors was reduced from thirty-two to five. These methods suffer from the following drawbacks: a large amount of data is needed in order to select proper sensors; and the available data must satisfy a typical distribution such as normal (or Gaussian) distribution. The performance of the proposed ANFIS model was compared with a traditional ANN model
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