A Linear Regression Thermal Displacement Lathe Spindle Model
TLDR
In this paper, a thermal deformation compensation model is presented that can reduce the influence of spindle thermal error on machining accuracy, which is one of the main reasons for the loss of accuracy in lathe machining.Abstract:
Thermal error is one of the main reasons for the loss of accuracy in lathe machining. In this study, a thermal deformation compensation model is presented that can reduce the influence of spindle thermal error on machining accuracy. The method used involves the collection of temperature data from the front and rear spindle bearings by means of embedded sensors in the bearing housings. Room temperature data were also collected as well as the thermal elongation of the main shaft. The data were used in a linear regression model to establish a robust model with strong predictive capability. Three methods were used: (1) Comsol was used for finite element analysis and the results were compared with actual measured temperatures. (2) This method involved the adjustment of the parameters of the linear regression model using the indicators of the coefficient of determination, root mean square error, mean square error, and mean absolute error, to find the best parameters for a spindle thermal displacement model. (3) The third method used system recognition to determine similarity to actual data by dividing the model into rise time and stable time. The rise time was controlled to explore the accuracy of prediction of the model at different intervals. The experimental results show that the actual measured temperatures were very close to those obtained in the Comsol analysis. The traditional model calculates prediction error values within single intervals, and so the model was divided to give rise time and stable time. The experimental results showed two error intervals, 19µm in the rise time and 15µm in the stable time, and these findings allowed the machining accuracy to be enhanced.read more
Citations
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Spindle Thermal Error Prediction Based on LSTM Deep Learning for a CNC Machine Tool
TL;DR: A key temperature point selection algorithm and thermal error estimation method for spindle displacement in a machine tool and the proposed long short-term memory model can provide improved accuracy and robustness in predicting the spindle thermal displacement are demonstrated.
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Thermal Error Analysis of Five-Axis Machine Tools Based on Five-Point Test Method
TL;DR: Wang et al. as mentioned in this paper proposed a method to measure the thermal error of the machine tool spindle using the five-point test method, which provides a theoretical basis and practical method for the measurement of thermal errors on five-axis machine tools.
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