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1.中南大学 极端服役性能精准制造全国重点试验室,长沙 410083
2.中南大学 机电工程学院,长沙 410083
3.中国航发湖南动力机械研究所,株洲 412002
4.中国航发中传机械有限公司,长沙 410200
侯津博,男,2000年生,天津市人,硕士研究生;主要研究方向为内啮合珩齿;csuhjb417@163.com。
收稿:2025-11-06,
修回:2025-12-10,
网络首发:2026-04-30,
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侯津博,唐进元,周伟华,等.航空圆柱齿轮内啮合抛光珩齿三维表面粗糙度预测与试验验证[J].机械传动,XXXX,XX(XX):1-9.
HOU Jinbo,TANG Jinyuan,ZHOU Weihua,et al.Prediction method for the three-dimensional surface roughness in internal gear polish honing of the aeronautic cylindrical gear[J].Journal of Mechanical Transmission,XXXX,XX(XX):1-9.
侯津博,唐进元,周伟华,等.航空圆柱齿轮内啮合抛光珩齿三维表面粗糙度预测与试验验证[J].机械传动,XXXX,XX(XX):1-9. DOI:
HOU Jinbo,TANG Jinyuan,ZHOU Weihua,et al.Prediction method for the three-dimensional surface roughness in internal gear polish honing of the aeronautic cylindrical gear[J].Journal of Mechanical Transmission,XXXX,XX(XX):1-9. DOI:
目的
2
内啮合抛光珩齿,具有提高齿面质量、降低传动噪声的特点,可以有效提升齿轮的使役性能和疲劳寿命。为深入理解航空齿轮内啮合抛光珩齿表面形成机制,指导工艺参数优化,研究提出内啮合抛光珩齿三维表面粗糙度的正向预测方法。
方法
2
首先,基于共轭啮合原理及磨粒切削机制,推导出珩齿工艺参数驱动下的磨粒切削深度与切削次数;其次,将珩磨过程中磨粒对齿面微观凸峰的渐进去除,等效为作用于三维高度数据的“啮合接触-材料去除”信号处理算子;然后,通过对实测初始齿面进行自适应降噪与形貌保真修正,模拟材料去除过程;最后,基于Matlab软件实现迭代计算,获得珩磨后齿面的三维高度数据并预测粗糙度。
结果
2
经珩齿试验验证,4组样本中
S
a
、
S
q
、
S
z
三类粗糙度参数的预测平均误差分别为6.42%、5.29%和5.86%,最大误差不超过12.76%。分析表明,表面粗糙度随珩磨轮转速增加而降低,随切削率降低而提高。研究为内啮合抛光珩齿工艺参数优化提供了定量参考,也为复杂曲面零件的精密加工提供了新的主动预测方法。
Objective
2
Internal gear polish honing has the characteristics of improving tooth surface quality and reducing transmission noise
which can effectively improve the service performance and fatigue life of gears. In order to gain a deeper understanding of the surface formation mechanism of internal gear polish honing of aerospace gears and guide the optimization of process parameters
a positive prediction method for the three-dimensional surface roughness of internal gear polish honing was proposed.
Methods
2
Firstly
based on the principles of internal conjugate meshing and abrasive cutting mechanisms
the cutting depth and cutting number of abrasive grains driven by honing process parameters were derived. Secondly
the progressive removal of micro-asperities on the tooth surface by abrasives during honing was equivalently mapped into a "meshing contact-material removal" signal processing operator applied to the 3D height data. Then
through adaptive denoising and morphology-preserving correction of the measured initial tooth surface
the material removal process was simulated. Finally
iterative calculations were performed using Matlab software to obtain the post-honing 3D surface height data and predict the roughness.
Results
2
Validated by gear honing experiments
the average prediction errors for 12 groups of roughness parameters
S
a
、
S
q
and
S
z
are 6.41%
5.30%
and 5.86%
respectively
with the maximum error not exceeding 12.76%. The analysis indicates that surface roughness decreases with an increase in honing wheel speed and increases with a decrease in
the cutting rate. This research provides a quantitative reference for optimizing process parameters in internal gear polish honing and offers a novel proactive prediction method for the precision machining of complex curved surfaces.
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