• CN: 11-2187/TH
  • ISSN: 0577-6686

Journal of Mechanical Engineering ›› 2022, Vol. 58 ›› Issue (21): 250-265.doi: 10.3901/JME.2022.21.250

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Modeling of Probe Comprehensive Pre-travel Error For On-machine Inspection System of Gear Grinder

YANG Yongming1, WANG Zhonghou1, LIU Lei2, SHI Zhaoyao3, AIZOH K4   

  1. 1. School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093;
    2. School of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016;
    3. Beijing Engineering Research Center of Precision Measurement Technology and Instruments, Beijing University of Technology, Beijing 100124;
    4. Research Institute for Applied Sciences, Kyoto 606-8202 Japan
  • Received:2021-11-19 Revised:2022-09-01 Online:2022-11-05 Published:2022-12-23

Abstract: Due to the existence of probe pre-travel error, the existing researches consider single or double influencing factors for error compensation. However, many experimental statistics show that, due to the anisotropy of touch trigger probe, factors such as signal transmission delay, inspection speed, ball radius, rod length, probe gravity and normal vector of measuring point on ball surface will affect the trigger time of inspection signal, resulting in probe comprehensive pre-travel error, so it is difficult to compensate accurately. When inputs are multiple error influencing factors, the efficient approximation algorithm of BP neural network is helpful to solve the measurement error output problem, so as to improve on-machine inspection accuracy effectively. According to the on-machine inspection principle of independently developed horizontal gear grinder L300G, taking multiple error influencing factors as the input node and probe comprehensive pre-travel error as the output node, then the prediction model of probe comprehensive pre-travel error based on BP neural network is established. After error compensation, the standard sample gear on-machine inspections of gear grinder were carried out. Results show that, before and after error compensation, the tooth lead accuracies are both at level 4; after error compensation, the tooth profile accuracies are improved by 2 grades and are at level 4, which is consistent with that of Gleason inspections. Conclusion verifies the correctness of model, which is expected to be popularized in the high-precision on-machine inspection system of domestic low-cost gear grinder.

Key words: probe comprehensive pre-travel error, BP neural network, error compensation, on-machine inspection, gear grinder

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