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Vol. 34, No. 8(3), S&M3042

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Vol. 32, No. 8(2), S&M2292

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Sensors and Materials
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Sensors and Materials, Volume 32, Number 3(1) (2020)
Copyright(C) MYU K.K.
pp. 859-872
S&M2143 Research Paper of Special Issue
Published: March 10, 2020

Prediction of Thermal Deformation of Rotary Table in Multifunction Machine Tool Using Neural Networks [PDF]

Shao-Hsien Chen and Wun-Syuan Huang

(Received January 18, 2019; Accepted December 29, 2019)

Keywords: rotation table, temperature rise, thermal deformation, backpropagation of artificial neural network

The five-axis machining center and mill-turn lathe are some of the modern machining technologies widely used around the world. The spindle of the mill-turn lathe is the power source for cutting and milling. The spindle often spins at 2000 rpm or more for higher milling accuracy and efficiency. However, as the rotation speed increases, so does the temperature and, thus, the accuracy deteriorates and the number of errors increases. As a result, it is important to measure and predict the thermal deformation in the spindle of the mill-turn lathe. For this study, temperature was measured at various points on the spindle. The deformation was measured using a gantry-type main axis. The temperature increase and deformation measurements were analyzed, and the results were used for the prediction using the backpropagation of an artificial neural network. From this, the machining accuracy can be improved by refining the structure design or compensation. The largest temperature increase was found to be 8 °C. The maximum deformations were 0.026 mm for the X-axis, 0.004 mm for the Y-axis, and −0.069 mm for the Z-axis.

Corresponding author: Shao-Hsien Chen

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Cite this article
Shao-Hsien Chen and Wun-Syuan Huang, Prediction of Thermal Deformation of Rotary Table in Multifunction Machine Tool Using Neural Networks, Sens. Mater., Vol. 32, No. 3, 2020, p. 859-872.

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