基于伯努利分布与混合驱动模型的铣削稳定性高精判断方法

doi:10.3901/JME.2024.11.318

机械工程学报 ›› 2024, Vol. 60 ›› Issue (11): 318-331.doi: 10.3901/JME.2024.11.318

扫码分享

基于伯努利分布与混合驱动模型的铣削稳定性高精判断方法

谭志朴¹, 秦国华¹, 娄维达², 吴竹溪¹

1. 南昌航空大学航空制造工程学院南昌 330063;
2. 西北工业大学机电学院西安 710072

收稿日期:2023-03-11 修回日期:2023-09-05 出版日期:2024-06-05 发布日期:2024-08-02
作者简介:谭志朴,男,1998年出生。主要研究方向为机械加工质量分析与智能诊断等。E-mail:526510948@qq.com
秦国华(通信作者),男,1970年出生,教授,博士研究生导师。主要研究方向为工件装夹分析与优化、加工过程力学分析与仿真、残余应力分析与预测、健康管理与故障诊断等。E-mail:qghwzx@126.com
娄维达,男,1994年出生,博士研究生。主要研究方向为铣削稳定性分析与抑制、高效高精加工技术。
吴竹溪,女,1969年出生,副教授。主要研究方向为数控加工技术、高效高精加工技术。
基金资助:
国家自然科学基金(51765047)、江西省主要学科学术和技术带头人培养计划(20172BCB22013)和江西省重点研发计划(20203BBE53049)资助项目。

High Precision Judgement Method for Milling Stability Based on Bernoulli Distribution and Hybrid Drive Model

TAN Zhipu¹, QIN Guohua¹, LOU Weida², WU Zhuxi¹

1. School of Aeronautical Manufacturing Engineering, Nanchang Hangkong University, Nanchang 330063;
2. School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an 710072

Received:2023-03-11 Revised:2023-09-05 Online:2024-06-05 Published:2024-08-02

摘要/Abstract

摘要： 铣削颤振严重影响工件表面质量和生产效率，准确识别铣削稳定域对于抑制颤振、提高生产效率具有非常重要的意义。目前一般依据状态转移矩阵特征值的谱半径小于1，来判断铣削系统的稳定性。由于在铣削和锤击实验的不确定性，铣削系统的属性参数势必存在误差，极大地影响稳定性的判断结果。为此通过研究铣削过程颤振稳定性的概率特性，建立属性参数优化的SLD(稳定性叶瓣图)修正方法。首先利用Cotes积分法和Floquet理论，建立铣削稳定性的判断模型。其次利用伯努利分布概率函数等效描述稳定性判断模型，根据稳定性判断结果的伯努利分布规律提出属性参数的修正函数。最后提出基于神经网络的黄金分割法和遗传算法的属性参数优化方法，实验结果表明，提出的方法对单个叶瓣的稳定性判断准确率由69.77%提高到93.02%，整个SLD的准确率可由73.73%提升至87.29%。

关键词: 颤振, Cotes积分, 稳定性叶瓣图, 伯努利分布, 数据驱动法, 优化

Abstract: Milling chatter seriously affects workpiece surface quality and production efficiency. It is very important to identify milling stability domain accurately for suppressing chatter and improving production efficiency. At present, the stability of milling system is generally judged according to the spectral radius of eigenvalue of state transition matrix is less than 1. Due to the uncertainty in milling and hammering experiments, there exist errors to affect milling system attribute parameters and in turn, greatly affect the judgment results of stability. Therefore, by studying the probabilistic characteristics of chatter stability in milling process, a correction method of SLD (stability lobe diagram) based on attribute parameter optimization is established. Firstly, a milling stability judgment model is established by using Cotes integral method and Floquet theory. Secondly, the stability judgment model is equivalent described by Bernoulli distribution probability function. The correction functions of attribute parameters are proposed according to Bernoulli distribution law of stability judgment results. Finally, the golden section method and genetic algorithm based on the neural network are proposed to optimize the attribute parameters. The experimental results show that the stability accuracy of the proposed method for a single lobe is improved from 69.77% to 93.02% whereas the accuracy of the whole SLD is improved from 73.73% to 87.29%.

Key words: chatter, Cotes integration, stability lobe diagram, Bernoulli distribution, data driven method, optimization

中图分类号:

TH161

谭志朴, 秦国华, 娄维达, 吴竹溪. 基于伯努利分布与混合驱动模型的铣削稳定性高精判断方法[J]. 机械工程学报, 2024, 60(11): 318-331.

TAN Zhipu, QIN Guohua, LOU Weida, WU Zhuxi. High Precision Judgement Method for Milling Stability Based on Bernoulli Distribution and Hybrid Drive Model[J]. Journal of Mechanical Engineering, 2024, 60(11): 318-331.

参考文献

[1] ALTINTAS Y,WECK M.Chatter stability of metal cutting and grinding[J].CIRP Annals,2004,53:619-642.
[2] TOBIAS S A.Machine tool vibration research[J].International Journal of Machine Tool Design and Research,1961,1:1-14.
[3] SCHMITZ T L,SMITH K S.Machining dynamics[M].Boston,MA:Springer US,2009.
[4] KURDI M H,SCHMITZ T L,HAFTKA R T,et al.A numerical study of uncertainty in stability and surface location error in high-speed milling[C]//Proceedings of the ASME 2005 International Mechanical Engineering Congress and Exposition.Manufacturing Engineering and Materials Handling,Parts A and B.Orlando,Florida,USA.November 5-11,2005:387-395.
[5] ZHANG X M,ZHU L M,ZHANG D,et al.Numerical robust optimization of spindle speed for milling process with uncertainties[J].International Journal of Machine Tools and Manufacture,2012,61:9-19.
[6] HUANG X Z,HU M W,ZHANG Y M,et al.Probabilistic analysis of chatter stability in turning[J].International Journal of Advanced Manufacturing Technology,2016,87:3225-3232.
[7] TOTIS G.RCPM-a new method for robust chatter prediction in milling[J].International Journal of Machine Tools and Manufacture,2009,49:273-284.
[8] BUDAK E,ALTINTAS Y.Analytical prediction of chatter stability in milling-part I:General formulation[J].Journal of Dynamic Systems,Measurement,and Control,1998,120:22-30.
[9] INSPERGER T,STÉPÁN G.Semi-discretization method for delayed systems[J].International Journal for Numerical Methods in Engineering,2002,55:503-518.
[10] DING Y,ZHU L M,ZHANG X J,et al.A full-discretization method for prediction of milling stability[J].International Journal of Machine Tools and Manufacture,2010,50:502-509.
[11] QIN C J,TAO J F,LIU C L.Stability analysis for milling operations using an Adams-Simpson-based method[J].International Journal of Advanced Manufacturing Technology,2017,92:969-979.
[12] JI Y J,WANG X B,LIU Z B,et al.An updated full-discretization milling stability prediction method based on the higher-order Hermite-Newton interpolation polynomial[J].International Journal of Advanced Manufacturing Technology,2018,95:2227-2242.
[13] LI W T,WANG L P,YU G.An accurate and fast milling stability prediction approach based on the Newton-Cotes rules[J].International Journal of Mechanical Sciences,2020,177:105469.
[14] DONG X F,QIU Z Z.Stability analysis in milling process based on updated numerical integration method[J].Mechanical Systems and Signal Processing,2020,137:106435.
[15] SCHMITZ T L,DONALSON R R.Predicting high-speed machining dynamics by substructure analysis[J].CIRP Annals,2000,49:303-308.
[16] ÖZŞAHIN O,ÖZGÜVEN H N,BUDAK E.Analysis and compensation of mass loading effect of accelerometers on tool point FRF measurements for chatter stability predictions[J].International Journal of Machine Tools and Manufacture,2010,50:585-589.
[17] ÖZŞAHIN O,ALTINTAS Y.Prediction of frequency response function (FRF) of asymmetric tools from the analytical coupling of spindle and beam models of holder and tool[J].International Journal of Machine Tools and Manufacture,2015,92:31-40.
[18] EYNIAN M.In-process identification of modal parameters using dimensionless relationships in milling chatter[J].International Journal of Machine Tools and Manufacture,2019,143:49-62.
[19] DANG J W,ZHANG W H,YANG Y,et al.Cutting force modeling for flat end milling including bottom edge cutting effect[J].International Journal of Machine Tools and Manufacture,2010,50:986-997.
[20] KARANDIKAR J,HONEYCUTT A,SCHMITZ T,et al.Stability boundary and optimal operating parameter identification in milling using Bayesian learning[J].Journal of Manufacturing Processes,2020,56:1252-1262.
[21] POSTEL M,BUGDAYCI B,WEGENER K.Ensemble transfer learning for refining stability predictions in milling using experimental stability states[J].International Journal of Advanced Manufacturing Technology,2020,107:4123-4139.
[22] CHEN G X,LI Yi G,LIU X,et al.Physics-informed Bayesian inference for milling stability analysis[J].International Journal of Machine Tools and Manufacture,2021,167:103767.
[23] LOU W D,QIN G H,ZUO D W.Investigation on Cotes-formula-based prediction method and its experimental verification of milling stability[J].Journal of Manufacturing Processes,2021,64:1077-1088.
[24] TOTIS G,SORTINO M.Polynomial chaos-kriging approaches for an efficient probabilistic chatter prediction in milling[J].International Journal of Machine Tools and Manufacture,2020,157:103610.
[25] CHERUKURI H,PEREZ-BERNABEU E,SELLES M,et al.Machining chatter prediction using a data learning model[J].Journal of Manufacturing and Materials Processing,2019,3(2):3020045.
[26] CUEVAS E,ENRÍQUEZ L,ZALDÍVAR D,et al.A selection method for evolutionary algorithms based on the golden section[J].Expert Systems with Applications,2018,106:183-196.
[27] BANAEI A,ALAMATIAN J,ZIA-TOHIDI R.Active control of structures using genetic algorithm with dynamic weighting factors using in the constrained objective function[J].Structures,2022,47:189-200.
[28] HAJDU D,BORGIOLI F,MICHIELS W,et al.Robust stability of milling operations based on pseudospectral approach[J].International Journal of Machine Tools and Manufacture,2020,149:103516.

基于伯努利分布与混合驱动模型的铣削稳定性高精判断方法

High Precision Judgement Method for Milling Stability Based on Bernoulli Distribution and Hybrid Drive Model

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	刘鑫, 张俊, 徐斌斌, 刘弘光, 赵万华. 激光辅助铣削过程的预热温度场调控方法研究[J]. 机械工程学报, 2024, 60(9): 218-228.
[2]	魏荣, 徐默然, 李常平, 李树健, 李鹏南. 电火花辅助铣削钛合金多能场建模及调控优化研究[J]. 机械工程学报, 2024, 60(9): 393-409.
[3]	杨柳, 高天雄, 宋颜和, 孔祥东, 张世林. 基于田口法的比例电磁铁极靴结构优化研究[J]. 机械工程学报, 2024, 60(8): 329-336.
[4]	张行, 富宽, 陈铭浩, 李睿, 石新娜. 压差式多节串联管道机器人越障时动力学演化规律及减振分析[J]. 机械工程学报, 2024, 60(8): 348-359.
[5]	赵希坤, 李聪波, 杨勇, 吕岩, 姜书艳. 数据-机理混合驱动下考虑刀具柔性的柔性加工工艺参数能效优化方法[J]. 机械工程学报, 2024, 60(7): 236-248.
[6]	郭江, 潘博, 连佳乐, 杨哲, 刘欢, 高菲, 康仁科. 双面研磨技术研究现状与发展趋势[J]. 机械工程学报, 2024, 60(7): 266-288.
[7]	刘阔, 姜业明, 黄任杰, 李明禹, 陈虎, 王永青. 虑及颤振在线抑制的机床高效自适应加工技术研究[J]. 机械工程学报, 2024, 60(6): 104-113.
[8]	姜文涛, 刘晓明, 李培源, 陈海, 张煦松. 机械式真空快速切换开关机构多目标优化设计[J]. 机械工程学报, 2024, 60(6): 153-162.
[9]	周晨, 徐飞翔. 四轮独立转向车辆转向模式切换轨迹多目标优化方法研究[J]. 机械工程学报, 2024, 60(6): 306-320.
[10]	柴依扬, 张乐乐, 窦伟元, 张海峰. 基于并行NSGA-III算法的高速列车车体侧墙结构高维多目标优化[J]. 机械工程学报, 2024, 60(6): 321-333.
[11]	周祖意, 杨玉维, 齐文耀, 龚健超, 李照童. 腰部外骨骼机器人多刚-柔体动力学等效逆解方法研究及其性能优化综合[J]. 机械工程学报, 2024, 60(5): 107-118.
[12]	赵承伟, 张文豪, 龚天诚, 张逸云, 王长涛, 罗先刚. 平面光学元件超平整吸附装载的优化设计与分析[J]. 机械工程学报, 2024, 60(5): 241-248.
[13]	张鹏, 尹来容, 仝永刚, 黄龙. 基于间隙预测模型的自动铺带轨迹优化方法[J]. 机械工程学报, 2024, 60(5): 296-316.
[14]	高强, 王健, 张严, 郑旭阳, 吕昊, 殷国栋. 拓扑优化方法及其在运载工程中的应用与展望[J]. 机械工程学报, 2024, 60(4): 369-390.
[15]	纵怀志, 艾吉昆, 张军辉, 江磊, 谭树杰, 刘余贤, 苏琦, 徐兵. 基于拓扑优化和晶格填充的四足机器人肢腿单元轻量化设计[J]. 机械工程学报, 2024, 60(4): 420-429.