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Open AccessArticle

Parameter Estimation of the Thermal Network Model of a Machine Tool Spindle by Self-made Bluetooth Temperature Sensor Module

1
Institute of Applied Mechanics, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., Taipei 10617, Taiwan
2
Department of Mechanical and Electromechanical Engineering, National ILan University, No.1, Sec. 1, Shennong Rd., Yilan City, Yilan County 260, Taiwan
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(2), 656; https://doi.org/10.3390/s18020656
Received: 24 January 2018 / Revised: 15 February 2018 / Accepted: 21 February 2018 / Published: 23 February 2018
(This article belongs to the Special Issue Selected Sensor Related Papers from ICI2017)
Thermal characteristic analysis is essential for machine tool spindles because sudden failures may occur due to unexpected thermal issue. This article presents a lumped-parameter Thermal Network Model (TNM) and its parameter estimation scheme, including hardware and software, in order to characterize both the steady-state and transient thermal behavior of machine tool spindles. For the hardware, the authors develop a Bluetooth Temperature Sensor Module (BTSM) which accompanying with three types of temperature-sensing probes (magnetic, screw, and probe). Its specification, through experimental test, achieves to the precision ±(0.1 + 0.0029|t|) °C, resolution 0.00489 °C, power consumption 7 mW, and size Ø40 mm × 27 mm. For the software, the heat transfer characteristics of the machine tool spindle correlative to rotating speed are derived based on the theory of heat transfer and empirical formula. The predictive TNM of spindles was developed by grey-box estimation and experimental results. Even under such complicated operating conditions as various speeds and different initial conditions, the experiments validate that the present modeling methodology provides a robust and reliable tool for the temperature prediction with normalized mean square error of 99.5% agreement, and the present approach is transferable to the other spindles with a similar structure. For realizing the edge computing in smart manufacturing, a reduced-order TNM is constructed by Model Order Reduction (MOR) technique and implemented into the real-time embedded system. View Full-Text
Keywords: Bluetooth temperature sensor module; machine tool spindle; parameter estimation; predictive thermal characteristic; thermal network model; system identification Bluetooth temperature sensor module; machine tool spindle; parameter estimation; predictive thermal characteristic; thermal network model; system identification
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Lo, Y.-C.; Hu, Y.-C.; Chang, P.-Z. Parameter Estimation of the Thermal Network Model of a Machine Tool Spindle by Self-made Bluetooth Temperature Sensor Module. Sensors 2018, 18, 656.

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