车削颤振时变可靠性预测

doi:10.12068/j.issn.1005-3026.2017.05.016

东北大学学报:自然科学版 ›› 2017, Vol. 38 ›› Issue (5): 684-689.DOI: 10.12068/j.issn.1005-3026.2017.05.016

车削颤振时变可靠性预测

刘宇¹，王振宇¹，杨慧刚²，张义民¹

(1. 东北大学机械工程与自动化学院，辽宁沈阳110819; 2. 菏泽学院蒋震机电工程学院，山东菏泽274015)

收稿日期:2015-05-14 修回日期:2015-05-14 出版日期:2017-05-15 发布日期:2017-05-11
通讯作者: 刘宇
作者简介:刘宇(1980-)，男，宁夏中卫人，东北大学副教授; 张义民(1958-)男，吉林长春人，东北大学教授，教育部“长江学者奖励计划”特聘教授.
基金资助:
科技重大专项 (2013ZX04011011).

Chatter Reliability Prediction in the Turning Process with Time-Varying Stability

LIU Yu¹， WANG Zhen-yu¹， YANG Hui-gang²， ZHANG Yi-min¹

1. School of Mechanical Engineering & Automation， Northeastern University， Shenyang 110819， China; 2. Jiangzhen Electrical Engineering Institute， Heze University， Heze 274015， China.

Received:2015-05-14 Revised:2015-05-14 Online:2017-05-15 Published:2017-05-11
Contact: LIU Yu
About author:-
Supported by:
-

摘要/Abstract

摘要：

针对车削加工过程中刀具磨损使得切削力系数等加工参数不断变化，进而导致传统的颤振预测方法随着加工时间的增加预测精度大大降低的问题，将时变可靠性理论引入颤振预测中，用线性方程表示了合力切削力系数的均值随切削时间的变化关系，建立了时变稳定性和时变可靠性模型.获得给定加工条件下系统的时变极限切宽和时变颤振可靠度曲线.最后对提出的计算方法做了相应的实验证明.实验表明.提出的颤振时变可靠性预测方法能够更为准确地预测不同加工时间下的颤振.

关键词: 刀具磨损, 切削力系数, 时变随机参数, 颤振时变稳定度, 颤振时变可靠性

Abstract:

Tool wear in the machining process changes the cutting force coefficient and other parameters in flux， which causes the prediction accuracy of traditional chatter prediction to decrease greatly with the increase of processing time. The time-varying reliability theory was introduced to predict chatter reliability， and the linear equations were used to express the change inthe mean values of cutting force coefficient with varied cutting time whereby the chatter stability and reliability model in turning was presented. The time-varying critical cutting width and time-varying chatter reliability curve were obtained in the given machining conditions， and the corresponding calculation method was tested experimentally. The results showed that the proposed method can be used to predict the chatter of different processing time accurately.

Key words: tool wear, cutting force coefficient, time-varying random parameter, time-varying chatter reliability, time-varying chatter stability

中图分类号:

刘宇，王振宇，杨慧刚，张义民. 车削颤振时变可靠性预测[J]. 东北大学学报:自然科学版, 2017, 38(5): 684-689.

LIU Yu， WANG Zhen-yu， YANG Hui-gang， ZHANG Yi-min. Chatter Reliability Prediction in the Turning Process with Time-Varying Stability[J]. Journal of Northeastern University:Natural Science, 2017, 38(5): 684-689.

参考文献

[1]Lee L C，Lee K S，Gan C S.On the correlation between dynamic cutting force and tool wear［J］.International Journal of Machine Tools & Manufacture，1989，29(3):295-303.
[2]Albertelli P，Mussi V，Ravasio C，et al.An experimental investigation of the effects of spindle speed variation on tool wear in turning［J］.Procedia Cirp， 2012，4(11):29-34.
[3]Debnath S，Reddy M M，Yi Q S.Influence of cutting fluid conditions and cutting parameters on surface roughness and tool wear in turning process using Taguchi method［J］.Measurement，2016，78:111-119.
[4]Cascón I，Sarasua J A.Mechanistic model for prediction of cutting forces in turning of non-axisymmetric parts［J］.Procedia CIRP，2015，31:435-440.
[5]Che D，Ehmann K.Experimental study of force responses in polycrystalline diamond face turning of rock［J］.International Journal of Rock Mechanics & Mining Sciences，2014，72:80-91.
[6]Campocasso S，Costes J P，Fromentin G，et al.A generalised geometrical model of turning operations for cutting force modelling using edge discretisation［J］.Applied Mathematical Modelling，2015，39(21):6612-6630.
[7]Li L，An Q.An in-depth study of tool wear monitoring technique based on image segmentation and texture analysis［J］.Measurement，2015，79:44-52.
[8]Rmili W，Ouahabi A，Serra R，et al.An automatic system based on vibratory analysis for cutting tool wear monitoring［J］.Measurement， 2016，77:117-123.
[9]Maia L H A，Abrao A M，Vasconcelos W L，et al.A new approach for detection of wear mechanisms and determination of tool life in turning using acoustic emission［J］.Tribology International， 2015，92:519-532.
[10]Maia L H A，Abrao A M，Vasconcelos W L，et al.A new approach for detection of wear mechanisms and determination of tool life in turning using acoustic emission［J］.Tribology International，2015，92:519-532.
[11]Katuku K，Koursaris A，Sigalas I.Wear，cutting forces and chip characteristics when dry turning ASTM grade 2 austempered ductile iron with PcBN cutting tools under finishing conditions［J］.Journal of Materials Processing Technology， 2009，209(5):2412-2420.
[12]Altintas Y，Mohammad R K.Generalized modeling of chip geometry and cutting forces in multi-point thread turning［J］.International Journal of Machine Tools & Manufacture，2015，98:21-32.
[13]Ahmed G M S，Quadri S S H，Mohiuddin M S.Optimization of feed and radial force in turning process by using Taguchi design approach［J］.Materials Today Proceedings，2015，2(4/5):3277-3285.
[14]Patidar L.Study of optimised process parameters in turning operation through force dynamometer on CNC machine［J］.Materials Today Proceedings，2015，2(4/5):2300-2305.
[15]Malakooti B，Wang J，Tandler W E.A sensor-based accelerated approach for multi-attribute machinability and tool life evaluation［J］.International Journal of Production Research，1990，28(12):2373-2392.
[16]Campocasso S，Costes J P，Fromentin G，et al.A generalised geometrical model of turning operations for cutting force modelling using edge discretisation［J］.Applied Mathematical Modelling，2015，39(21):6612-6630.
[17]Liu Y，Li T X，Liu K，et al.Chatter reliability prediction of turning process system with uncertainties［J］.Mechanical Systems & Signal Processing， 2016，66/67:232-247.
[18]Schmitz T L，Smith K S.Machining dynamics frequency response to improved productivity ［M］.New York:Springer Science Business Media，2009:122-124. (上接第683页)4结论1) 使用大型通用仿真软件对脉冲红外测试模型进行仿真，并将模型仿真数据与已知的实验数据进行了对比，确定了仿真数据的可行性.2) 在仿真模型的分析中发现影响最大温差时间的决定因素为缺陷半径、样板厚度及缺陷深度，同时也发现在缺陷半径相同的情况下，缺陷深度越小，相对温差的峰值越大，峰值温差对应的时间越短.3) 用量纲法推导了各个物理量之间的关系并由仿真数据得出一个近似的深度预测公式，能较为准确地识别缺陷深度与板厚比值小于0.5的板缺陷的缺陷深度.

车削颤振时变可靠性预测

Chatter Reliability Prediction in the Turning Process with Time-Varying Stability

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 9

编辑推荐

Metrics

本文评价

[1]	黄贤振，孙良仕，丁鹏飞，朱会彬. 基于可靠性的GH4169车削参数优化[J]. 东北大学学报（自然科学版）, 2022, 43(5): 696-702.
[2]	马廉洁，王馨，陈景强，周云光. 工程陶瓷车削刀具磨损机理及理论模型的研究[J]. 东北大学学报:自然科学版, 2020, 41(9): 1291-1296.
[3]	马廉洁，左宇辰，王馨，杨登峰. 氧化锆陶瓷车削刀具磨损机理及理论模型[J]. 东北大学学报:自然科学版, 2020, 41(7): 955-961.
[4]	王海艳，王健宇，陶克新. 碳纤维复合材料螺旋铣孔瞬时切削力系数识别[J]. 东北大学学报:自然科学版, 2020, 41(10): 1432-1437.
[5]	黎柏春，王振宇，张斌，王宛山. 球头铣刀切削刃存在差异的切削力系数辨识[J]. 东北大学学报:自然科学版, 2019, 40(9): 1316-1322.
[6]	恩溪弄，张义民，黄贤振. 车削颤振时变可靠性分析[J]. 东北大学学报:自然科学版, 2019, 40(10): 1442-1447.
[7]	巩亚东，梁彩霞，李强，刘洺君. 硬质合金刀具铣削镍基单晶高温合金DD5磨损试验[J]. 东北大学学报:自然科学版, 2018, 39(9): 1283-1288.
[8]	张锴锋，袁惠群，聂鹏. 基于广义维数与优化BP神经网络的刀具磨损量预测[J]. 东北大学学报(自然科学版), 2013, 34(9): 1292-1295.
[9]	崔敬巍;谢里阳;刘晓霞;. 应用MCEWMA控制图监视刀具磨损过程[J]. 东北大学学报(自然科学版), 2007, 28(5): 708-711.