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Study of thermal deformation monitoring system with long short term memory network in alignment turning system
Author(s): Chung-Ying Wang; Chien-Yao Huang; Jung Hsing Wang; Jun-Cheng Chen; Wei-Cheng Lin; Fong-Zhi Chen
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Paper Abstract

A thermal deformation monitoring system was developed in this study by applying the thermocouple sensors and capacitive displacement sensors, along with a Long Short Term Memory (LSTM) Network Model classifier, for the alignment turning system (ATS). An ATS can simultaneously provide the functions of measuring the centration error and dimensions of the lens cell in-line, and machining the lens barrel housing with reference to the lens optical axis. The ATS can manufacture precise lens cells, applied for optical metrology, high numerical aperture objective lenses, and lithography projection lenses. While rising temperature, the thermal error would occur on hydrostatic spindle which build in ATS. Therefore, the predetermined machining point would offset, thereby resulting in the machining error. In order to acquire the oil temperature of rotor and the relative thermal displacement between hydrostatic spindle and turret, the thermocouple sensors and capacitive displacement sensors were assembling on ATS. According to the measurement of oil temperature and relative displacement, the thermal deformation monitoring system of ATS hydrostatic spindle was established. Cause of the high resolution of capacitive displacement sensors, the more precise measurement values could be obtain so that the monitoring system would have higher accuracy. LSTM is a variant of Recurrent Neural Network (RNN) and could remember longer information changes than traditional RNN. The thermal deformation monitoring system with LSTM could be applied to compensate the thermal error to improve the workpiece quality in real-time, and also could save time and money of warming up centering machines in the future. Results shows that the mean square error (MSE) and RScore of forecasting thermal error is less than 0.0002 and higher than 0.997, which is highly accurate forecasting.

Paper Details

Date Published: 15 November 2019
PDF: 6 pages
Proc. SPIE 11175, Optifab 2019, 1117523 (15 November 2019); doi: 10.1117/12.2536380
Show Author Affiliations
Chung-Ying Wang, Taiwan Instrument Research Institute (Taiwan)
Chien-Yao Huang, Taiwan Instrument Research Institute (Taiwan)
Jung Hsing Wang, Taiwan Instrument Research Institute (Taiwan)
Jun-Cheng Chen, Taiwan Instrument Research Institute (Taiwan)
Wei-Cheng Lin, Taiwan Instrument Research Institute (Taiwan)
Fong-Zhi Chen, Taiwan Instrument Research Institute (Taiwan)


Published in SPIE Proceedings Vol. 11175:
Optifab 2019
Blair L. Unger; Jessica DeGroote Nelson, Editor(s)

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