磨齿机在机检测系统的测头综合预行程误差建模

doi:10.3901/JME.2022.21.250

机械工程学报 ›› 2022, Vol. 58 ›› Issue (21): 250-265.doi: 10.3901/JME.2022.21.250

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磨齿机在机检测系统的测头综合预行程误差建模

杨勇明¹, 汪中厚¹, 刘雷², 石照耀³, 久保爱三⁴

1. 上海理工大学机械工程学院上海 200093;
2. 南京航空航天大学机电学院南京 210016;
3. 北京工业大学北京市测控技术与仪器工程研究中心北京 100124;
4. 应用科学研究所京都 606-8202 日本

收稿日期:2021-11-19 修回日期:2022-09-01 出版日期:2022-11-05 发布日期:2022-12-23
通讯作者: 汪中厚(通信作者),男,1963年出生,博士,教授,博士研究生导师。主要研究方向为齿轮动态力学性能仿真、CAE及其应用。E-mail:wangzhusst@163.com
作者简介:杨勇明,男,1986年出生,博士研究生。主要研究方向为齿轮在机检测技术、齿轮磨削加工工艺、CAE及其应用。E-mail:jackyang@usst.edu.cn
基金资助:
国家自然科学基金（51875360）、南京航空航天大学“直升机传动技术重点实验室”开放课题（HTL-0-19G04）和上海市科学技术委员会（19060502300）资助项目。

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. 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

摘要/Abstract

摘要： 因为测头预行程误差的存在，现有研究大都考虑单项或双项影响因素进行误差补偿。然而多次的实验统计表明，由于接触式测头的各向异性，导致信号传输迟滞、检测速度、测球半径、测杆长度、测头重力及测球表面测点法矢等因素都会对检测信号的触发时机产生影响，因而存在测头综合预行程误差，故而很难进行精确补偿。借助BP神经网络的高效逼近算法，利于求解输入为多项误差影响因素场合的测量误差输出问题，有效提高在机检测精度。根据自主研发卧式磨齿机L300G在机检测原理，以多项误差影响因素为输入节点，以测头综合预行程误差为输出节点，建立基于BP神经网络的测头综合预行程误差预测模型。完成误差补偿后，开展磨齿机标准样板齿轮在机检测实验。结果表明：误差补偿前后，齿向精度均为4级；误差补偿后，齿形精度提高2个等级，为4级精度，与格里森检测结果相吻合。结果验证了模型的正确性，有望在国产低成本磨齿机的高精度在机检测系统中推广使用。

关键词: 测头综合预行程误差, BP神经网络, 误差补偿, 在机检测, 磨齿机

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

中图分类号:

TH161

杨勇明, 汪中厚, 刘雷, 石照耀, 久保爱三. 磨齿机在机检测系统的测头综合预行程误差建模[J]. 机械工程学报, 2022, 58(21): 250-265.

YANG Yongming, WANG Zhonghou, LIU Lei, SHI Zhaoyao, AIZOH K. Modeling of Probe Comprehensive Pre-travel Error For On-machine Inspection System of Gear Grinder[J]. Journal of Mechanical Engineering, 2022, 58(21): 250-265.

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磨齿机在机检测系统的测头综合预行程误差建模

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

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