Intelligent Manufacturing Technology

Thermal Error Compensation of the Wear-Depth Real-Time Detecting of Self-Lubricating Spherical Plain Bearings

  • Zhan-Qi Hu ,
  • Wei Li ,
  • Yu-Lin Yang ,
  • Bing-Li Fan ,
  • Hai-Li Zhou
展开
  • Aviation Key Laboratory of Science and Technology on Generic Technology of Aviation Self-Lubricating Spherical Plain Bearing, Yanshan University, Qinhuangdao 066004, China

收稿日期: 2016-03-18

  网络出版日期: 2019-07-23

基金资助

Supported by National Natural Science Foundation of China (Grant No. 51405422), Hebei Provincial Natural Science Foundation of China (Grant No. E2015203113), and Technological Innovation Fund of Aviation Industry of China (Grant No. 2014E00468R)

Thermal Error Compensation of the Wear-Depth Real-Time Detecting of Self-Lubricating Spherical Plain Bearings

  • Zhan-Qi Hu ,
  • Wei Li ,
  • Yu-Lin Yang ,
  • Bing-Li Fan ,
  • Hai-Li Zhou
Expand
  • Aviation Key Laboratory of Science and Technology on Generic Technology of Aviation Self-Lubricating Spherical Plain Bearing, Yanshan University, Qinhuangdao 066004, China

Received date: 2016-03-18

  Online published: 2019-07-23

Supported by

Supported by National Natural Science Foundation of China (Grant No. 51405422), Hebei Provincial Natural Science Foundation of China (Grant No. E2015203113), and Technological Innovation Fund of Aviation Industry of China (Grant No. 2014E00468R)

摘要

The spherical plain bearing test bench is a necessary detecting equipment in the research process of self-lubricating spherical plain bearings. The varying environmental temperatures cause the thermal deformation of the wear-depth detecting system of bearing test benches and then affect the accuracy of the wear-depth detecting data. However, few researches about the spherical plain bearing test benches can be found with the implementation of the detecting error compensation. Based on the self-made modular spherical plain bearing test bench, two main causes of thermal errors, the friction heat of bearings and the environmental temperature variation, are analysed. The thermal errors caused by the friction heat of bearings are calculated, and the thermal deformation of the wear-depth detecting system caused by the varying environmental temperatures is detected. In view of the above results, the environmental temperature variation is the main cause of the two error factors. When the environmental temperatures rise is 10.3 ℃, the thermal deformation is approximately 0.01 mm. In addition, the comprehensive compensating model of the thermal error of the wear-depth detecting system is built by multiple linear regression (MLR) and time series analysis. Compared with the detecting data of the thermal errors, the comprehensive compensating model has higher fitting precision, and the maximum residual is only 1 μm. A comprehensive compensating model of the thermal error of the wear-depth detecting system is proposed, which provides a theoretical basis for the improvement of the real-time wear-depth detecting precision of the spherical plain bearing test bench.

本文引用格式

Zhan-Qi Hu , Wei Li , Yu-Lin Yang , Bing-Li Fan , Hai-Li Zhou . Thermal Error Compensation of the Wear-Depth Real-Time Detecting of Self-Lubricating Spherical Plain Bearings[J]. Chinese Journal of Mechanical Engineering, 2018 , 31(5) : 87 -87 . DOI: 10.1186/s10033-018-0288-4

Abstract

The spherical plain bearing test bench is a necessary detecting equipment in the research process of self-lubricating spherical plain bearings. The varying environmental temperatures cause the thermal deformation of the wear-depth detecting system of bearing test benches and then affect the accuracy of the wear-depth detecting data. However, few researches about the spherical plain bearing test benches can be found with the implementation of the detecting error compensation. Based on the self-made modular spherical plain bearing test bench, two main causes of thermal errors, the friction heat of bearings and the environmental temperature variation, are analysed. The thermal errors caused by the friction heat of bearings are calculated, and the thermal deformation of the wear-depth detecting system caused by the varying environmental temperatures is detected. In view of the above results, the environmental temperature variation is the main cause of the two error factors. When the environmental temperatures rise is 10.3 ℃, the thermal deformation is approximately 0.01 mm. In addition, the comprehensive compensating model of the thermal error of the wear-depth detecting system is built by multiple linear regression (MLR) and time series analysis. Compared with the detecting data of the thermal errors, the comprehensive compensating model has higher fitting precision, and the maximum residual is only 1 μm. A comprehensive compensating model of the thermal error of the wear-depth detecting system is proposed, which provides a theoretical basis for the improvement of the real-time wear-depth detecting precision of the spherical plain bearing test bench.

参考文献

[1] X W Qi, J Ma, Z N Jia, et al. Effects of weft density on the friction and wear properties of self-lubricating fabric for journal bearings under heavy load conditions. Wear, 2014, 318(1): 124-129.
[2] Y Hu, Y T Fei, W T Cheng. Thermal deformation error and correction for articulated arm coordinate-measuring machines. Journal of Mechanical Engineering, 2011, 47(24): 15-19. (in Chinese)
[3] M Weck, P Mckeown, R Bonse, et al. Reduction and compensation of thermal errors in machine tools. CIRP Annals—Manufacturing Technology, 1995, 44(2): 589-598.
[4] K Zhou. Design for the test machine of spherical plain bearing and the accuracy compensation. Qinhuangdao: Yanshan University, 2012. (in Chinese)
[5] Z Q Hu, W Li, Y L Yang, et al. The high and low temperature environment test bench of spherical plain bearings: China, CN103048136A. 2013-4-17[2016-8-16]. http://d.g.wanfangdata.com.cn/Patent_CN201210556215.2.aspx.
[6] W Li, Z Q Hu, Y L Yang, et al. Comprehensive error modeling of real-time wear-depth detecting of spherical plain bearing tester. Optics and Precision Engineering, 2016, 24(4): 844-854. (in Chinese)
[7] Y Hong, N Jun. Dynamic neural network modeling for nonlinear, nonstationary machine tool thermally induced error. International Journal of Machine Tools & Manufacture, 2005, 45(4): 455-465.
[8] J Han, L P Wang, H T Wang, et al. A new thermal error modeling method for CNC machine tools. International Journal of Advanced Manufacturing Technology, 2011, 62(1-4): 205-212.
[9] W L Feng, Z H Li, Q Y Gu. Thermally induced positioning error modelling and compensation based on thermal characteristic analysis. International Journal of Machine Tools & Manufacture, 2015, 93: 26-36.
[10] T Reddy, V Shanmugaraj, V Prakash, et al. Real-time thermal error compensation module for intelligent ultra precision turning machine. Procedia Materials Science, 2014, 6: 1981-1988.
[11] B Tan, X Y Mao, H Q Liu. A thermal error model for large machine tools that considers environmental thermal hysteresis effects. International Journal of Machine Tools & Manufacture, 2014, 82-83(7): 11-20.
[12] A M Abdulshahed, A P Longstaff, S Fletcher. The application of ANFIS prediction models for thermal error compensation on CNC machine tools. Applied Soft Computing, 2015, 27(c): 158-168.
[13] A M Abdulshahed, A P Longstaff, S Fletcher. Thermal error modelling of machine tools based on ANFIS with fuzzy c-means clustering using a thermal imaging camera. Applied Mathematical Modelling, 2014, 39(7): 1837-1852.
[14] R Ramesh, M A Mannan, A N Poo, et al. Thermal error measurement and modelling in machine tools. Part Ⅱ. hybrid Bayesian network-support vector machine model. International Journal of Machine Tools & Manufacture, 2003, 43(4): 405-419.
[15] H Wu, H T Zhang, Q J Guo. Thermal error optimization modeling and real-time compensation on a CNC turning center. Journal of Materials Processing Technology, 2008, 207(1-3): 172-179.
[16] Q J Guo, J G Yang, H Wu. Application of ACO-BPN to thermal error modeling of NC machine tool. International Journal of Advanced Manufacturing Technology, 2010, 50(5): 667-675.
[17] W Li, Z Q Hu Z, Y L Yang, et al. Modeling and verification of comprehensive errors of real-time wear-depth detecting for spherical plan bearing tester. Journal of Central South University, 2018, 3(3): 533-545.
[18] D Zhang. Frictional heat temperature field simulation and test of self-lubricating spherical plain bearing. Qinhuangdao: Yanshan University, 2012. (in Chinese)
[19] T Y Chen, W J Wei, J C Tsai. Optimum design of headstocks of precision lathes. International Journal of Machine Tools & Manufacture, 1999, 39(12): 1961-1977.
[20] S Z Yang, Y Wu, J P Xuan. Time series analysis in engineering application. Wuhan: Huazhong University of Science and Technology Press, 2004. (in Chinese)
[21] H H Lin. Data processing of dynamic measurement. Beijing: Beijing Institute of Technology Press, 1995. (in Chinese)
[22] Z G Liu, Z J Cai, X M Tan. Forecasting research of aero-engine rotate speed signal based on arma model. Procedia Engineering, 2011, 15: 115-121.
文章导航

/