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5 November 2020 Research on thermal induced preload of machine tool spindle bearings based on FBG sensor
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Proceedings Volume 11569, AOPC 2020: Optical Information and Network; 115690J (2020) https://doi.org/10.1117/12.2580115
Event: Applied Optics and Photonics China (AOPC 2020), 2020, Beijing, China
Abstract
As the key factor that affects the performance of the spindle bearing, the thermal induced preload determines the running status of the bearing directly. Hence, the temperature rises of the spindle unit components, especially the spindle bearing rings, cause thermal deformation of the spindle bearing and the bearing housing, resulting in thermal induced preload. The real-time monitoring of the spindle bearing preload is the crucial step for obtaining the optimum bearing preload, which can improve the service performance of the spindle unit and the machine tool. However, at the present stage, the monitoring methods are mostly completed by the electric sensors in the experimental state, and the real-time preload cannot be achieved under the spindle working process. This article breaks through the limitations of the existing methods, taking advantages of fiber gratings sensors “one line multi-point, passive multi-field”, small size and corrosion resistance, et al. Based on the fiber Bragg grating (FBG) sensors embedded in the spindle, the online force test system realizes the measurement of the bearing preload in real-time.The relationship between spindle speed and thermal induced preload is analyzed. The results of two kinds of sensors were compared and the consistency was verified the accuracy of the test results of the FBG sensor and the feasibility of real-time measurement of the thermal induced preload of the spindle bearing.
© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Tuanliang Lu, Ming Qiu, and Yanfang Dong "Research on thermal induced preload of machine tool spindle bearings based on FBG sensor", Proc. SPIE 11569, AOPC 2020: Optical Information and Network, 115690J (5 November 2020); https://doi.org/10.1117/12.2580115
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