面齿轮展成磨削表面残余应力优化方法
Optimization method for residual stress in face gear generating grinding
陈冠峰 1马晓帆 2蔡志钦 3姚斌3
作者信息
- 1. 厦门大学嘉庚学院机电工程与自动化学院,福建 漳州 363105
- 2. 中北大学航空宇航学院,山西 太原 030051
- 3. 厦门大学航空航天学院,福建 厦门 361102
- 折叠
摘要
面齿轮展成磨削表面残余应力优化是一个复杂的非线性问题,传统的优化算法无法实现高效率和高精度的求解.因此,文章提出了以齿面磨削实验数据为驱动的面齿轮表面残余应力智能优化方法.通过设计系列实验获取齿面残余应力数据集,建立以基础数据为驱动的响应曲面模型,实现了以齿面残余应力和磨削效率为优化目标的智能粒子群多目标优化,解决了面齿轮表面残余应力的优化问题.实验表明该方法的相对误差介于 0.89%～1.61%,证明了面齿轮展成磨削表面残余应力优化方法的有效性.分析发现:齿面残余压应力与磨削深度和工件速度呈正相关,与砂轮速度和砂轮分度角呈负相关.从敏感性角度分析,齿面残余应力对磨削深度的敏感性最大,其次是砂轮分度角,而齿面残余应力对砂轮速度和工件速度的敏感性相对较小.
Abstract
The optimization of residual stress on the surface of face gear grinding is a complex nonlinear problem,and traditional optimization algorithms cannot achieve efficient and accurate solutions.Therefore,this paper proposes an intelligent optimization method for residual stress on the surface of face gear driven by experimental data of tooth surface grinding.By designing a series of experiments to obtain the tooth surface residual stress data set,a response surface model driven by basic data was established,and intelligent particle swarm multi-objective optimization with tooth surface residual stress and grinding efficiency as the optimization goals was realized,solving the problem of face gear surface Residual stress optimization problem.Experiments show that the relative error of this method ranges from 0.89%to 1.61%,which proves the effectiveness of the residual stress optimization method for surface gear generation grinding.The analysis found that the residual compressive stress on the tooth surface is positively correlated with the grinding depth and workpiece speed,and negatively correlated with the grinding wheel speed and grinding wheel feed angle.From the sensitivity analysis,the sensitivity of residual stress on grinding depth is the highest,followed by the grinding wheel feed angle,while the sensitivity of residual stress on grinding wheel speed and workpiece speed is relatively small.
关键词
面齿轮/展成磨削/残余应力/优化方法Key words
face gear/generating grinding/residual stress/optimization method引用本文复制引用
基金项目
福建省中青年教师教育科研项目(JAT220510)
广东省基础与应用基础研究基金(2023A1515010040)
福建省自然科学基金(2023J01048)
出版年
2024
NETL
NSTL
万方数据