基于切削运动学分析的铣削加工表面粗糙度预测方法研究

doi:10.3901/JME.2023.13.314

摘要/Abstract

摘要： 基于切削过程中的刀刃运动轨迹对加工表面形貌生成过程开展了运动学分析，首先基于刀具几何形状建立了面铣刀几何模型，并结合进给量、切削深度等切削参数和刀具偏心跳动带来的影响，建立了工件坐标系下的切削刃运动轨迹方程。其次对加工表面进行了离散化处理，并使用切比雪夫多项式求解计算得到每个网格处的表面形貌高度，并叠加取最小高度值作为最终的加工表面形貌高度。最后将单次铣削的预测结果与切削宽度结合，从而扩展到多次横向走刀铣削加工，提出了用于面铣刀几何形状和切削参数的表面粗糙度预测方法。通过不同平面铣削参数下的试验测量值对预测结果进行了验证，结果表明，同时考虑刀具几何形状与偏心跳动影响，预测值能够较为准确地表达出实际加工表面粗糙度的变化趋势与大小。

关键词: 铣削, 表面粗糙度, 表面形貌, 预测方法, 切削运动学

Abstract: Based on the cutting-edge motion trajectory in the cutting process, the kinematic analysis of the machining surface topography generation process is carried out. Firstly, a geometric model of the face milling cutter is established based on the cutting tool geometry, the cutting parameters such as feed rate, depth of cut, also with the cutting tool eccentricity runout have been combined. The model of the trajectory equation under the workpiece coordinate system is established which according to the motion of milling tool cutting edge. Secondly, the surface of the workpiece is discretized and Chebyshev polynomial is used to calculate the height of surface profile at each grid. The minimum value of these height is selected as the final height of the machined surface topography. Finally, combining with the cutting width, the prediction result of a single milling process is expanded to multiple transverse feed processing, a surface roughness prediction method for face mill geometries and milling parameters is proposed. The prediction result has been verified under different experiments when using different face milling parameters. The results show that under the consideration of cutting tool geometry and eccentricity runout simultaneously, the variation tendency and size of machined surface roughness could be forecasted accurately when using the surface roughness prediction method.

Key words: milling, surface roughness, surface topography, prediction method, cutting kinematics

中图分类号:

TG506

郭国强, 杨博宇, 李建华, 成群林, 王大中, 林立芳, 沈彬. 基于切削运动学分析的铣削加工表面粗糙度预测方法研究[J]. 机械工程学报, 2023, 59(13): 314-324.

GUO Guoqiang, YANG Boyu, LI Jianhua, CHENG Qunlin, WANG Dazhong, LIN Lifang, SHEN Bin. Study on the Prediction Method of Milling Surface Roughness Based on Cutting Kinematics Analysis[J]. Journal of Mechanical Engineering, 2023, 59(13): 314-324.

参考文献

[1] 姜文,张恒,严由春,等. 马氏体不锈钢铣削加工刀具磨损及切削参数优化的实验研究[J]. 机械设计与制造工程,2018,47(6):30-33. JIANG Wen,ZHANG Heng,YAN Youchun,et al. The experimental research on tool wear and optimization of cutting parameter in milling martensite stainless steel[J]. Machine Design and Manufacturing Engineering,2018,47(6):30-33.
[2] MUSTAFA U,OSMAN A,TURAN G,et al. Surface roughness prediction of machine daluminum alloy with wire electrical discharge machining by different machine learning algorithms[J]. Journal of Materials Research and Technology,2020,9(6):12512-12524.
[3] LEILA P,NAEIME M,SHIMA Y. Evaluating the effect of repeated use of milling burs on surface roughness and adaptation of digitally fabricated ceramic veneers[J]. Heliyon,2021,7(4):1-8.
[4] 魏士亮,房丰洲,刘立飞,等. 铣削加工透波性Si₃N₄陶瓷表面质量研究[J]. 宇航材料工艺,2020,50(2):57-62. WEI Shiliang,FANG Fengzhou,LIU Lifei,et al. Effect of milling on the surface quality of wave-transmitting Si3N4 ceramics[J]. Aerospace Materials & Technology,2020,50(2):57-62.
[5] 于英钊,高军,郑光明,等. 高速干铣削高强钢铣削力及表面粗糙度研究[J]. 组合机床与自动化加工技术,2018(12):21-24. YU Yingzhao,GAO Jun,ZHENG Guangming,et al. Milling forces and surface roughness in high-speed dry milling of high-strength steel[J]. Modular Machine Tool & Automatic Manufacturing Technique,2018(12):21-24.
[6] UMA M R P,SURYAPAVAN C,VENKAT P K P,et al. Machine learning and statistical approach in modeling and optimization of surface roughness in wire electrical discharge machining[J]. Machine Learning with Applications,2021,6:1-11.
[7] 郑中鹏,金鑫,江振卫,等. 微小型车铣加工表面粗糙度分析及预测模型建立[J]. 导航与控,2019,18(5):91-98. ZHENG Zhongpeng,JIN Xin,JIANG Zhenwei,et al. Analysis and establishment of prediction model for surface roughness of micro turn-milling[J]. Navigation and Control,2019,18(5):91-98.
[8] SEPEHR N,MASOUD P. Prediction of surface roughness of various machining processes by a hybrid algorithm including time series analysis,wavelet transform and multi view embedding[J]. Measurement,2021,184:1-12.
[9] ARRAZOLA P J,ÖZEL T,UMBRELLO D,et al. Recent advances in modelling of metal machining processes[J]. CIRP Annals-Manufacturing Technology,2013,62(2):695-718.
[10] 侯军明,王保升,汪木兰. 端面车铣加工表面粗糙度预测模型的研究[J]. 工具技术,2016,50(6):99-102. HOU Junming,WANG Baosheng,WANG Mulan. Research on prediction model of surface roughness in turn-milling machining[J]. Tool Engineering,2016,50(6):99-102.
[11] CHEN W,XIE W,HUO D,et al. A novel 3D surface generation model for micro milling based on homogeneous matrix transformation and dynamic regenerative effect[J]. International Journal of Mechanical Sciences,2018,144:146-157.
[12] JING X,LI H,WANG J,et al. Modelling the cutting forces in micro-end-milling using a hybrid approach[J]. The International Journal of Advanced Manufacturing Technology,2014,73:1647-1656.
[13] 付中涛,杨文玉,张彦辉. 基于切削力预测模型的复杂曲面铣削进给速度优化[J]. 中国科学,2016,46(7):722-730. FU Zhongtao,YANG Wenyu,ZHANG Yanhui. Feedrate optimization of complex surface milling based on predictive model of cutting force[J],Science China,2016,46(7):722-730.
[14] SARAYUT N,SUTHEP B. Surface roughness prediction with chip morphology using fuzzy logic on milling machine[J]. Materials Today:Proceedings,2020,26(2):2357-2365.
[15] 王永鑫,张昌明. TC18钛合金钻削加工性能与响应面优化试验[J]. 中国科技论文,2019,14(8):841-845. WANG Yongxin,ZHANG Changming. Experimental study on drilling performance and response surface optimization of TC18 titanium alloy[J]. Chinese Sciencepaper,2019,14(8):841-845.
[16] 陆德光,张太华,徐卫平. 基于QGA-SVR的工件表面粗糙度预测和分析[J]. 机床与液压,2020,48(15):103-108. LU Deguang,ZHANG Taihua,XU Weiping. Prediction and analysis of workpiece surface roughness based on QGA-SVR[J]. Machine Tool & Hydraulics,2020,48(15):103-108.
[17] 阴艳超,丁卫刚. 切削加工表面粗糙度的多维多规则云预测方法[J]. 机械工程学报,2016,52(15):204-212. YIN Yanchao,DING Weigang. A novel cloud model prediction for surface roughness based on multidimensional & multi-rules reasoning[J]. Journal of Mechanical Engineering,2016,52(15):204-212.
[18] 李锋,李涌泉,李文科,等. 刀具走刀方式对TC11薄壁件铣削表面质量影响规律研究[J]. 表面技术,2017,46(7):250-254. LI Feng,LI Yongquan,LI Wenke,et al. Effect of tool path mode on milled surface quality of TC11 thin-walled parts[J]. Surface Technology,2017,46(7):250-254.
[19] 王亚霁,孙玉利,墨洪磊,等. 单晶硅透镜铣磨工艺参数优化研究[J]. 航空制造技术,2021,64(7):90-94. WANG Yaji,SUN Yuli,MO Honglei,et al. Study on optimization of grinding process parameters of monocrystalline silicon lens[J]. Aeronautical Manufacturing Technology,2021,64(7):90-94.
[20] SHIVANNA D M,KIRAN M B,VENKATESH G S,et al. Analyzing the effects of machining parameters on surface roughness of machined surfaces using vision system[J]. Materials Today:Proceedings,2021,47(14):4885-4890.
[21] 盖立武,吴查穆,张克栋. Inconel718镍基合金高速铣削表面粗糙度研究[J]. 组合机床与自动化加工技术,2021,10:147-150. GAI Liwu,WU Chamu,ZHANG Kedong. Research on surface roughness of Inconel 718 nickel-based alloy in high speed milling[J]. Modular Machine Tool & Automatic Manufacturing Technique,2021,10:147-150.
[22] 罗恒,王优强,张平. 基于单因素法对7A09铝合金铣削表面质量的研究[J]. 表面技术,2020,49(3):327-333. LUO Heng,WANG Youqiang,ZHANG Ping. Study on surface quality of 7A09 aluminum alloy milling based on single factor method[J]. Surface Technology,2020,49(3):327-333.
[23] PASICHNYI O O,LAVRINENKO V I. The influence of circumferential waviness of the diamond wheel working surface on the machined surface roughness[J]. Journal of Superhard Materials,2019,41:278-280.
[24] WANG Y J,HUANG H B,CHEN L G,et al. Choice of reference surfaces for machined surface roughness in milling of SiCp/Al composites[J]. Journal of Central South University,2014,21:4150-4156.
[25] 马利杰,王西彬,刘贯军,等. 振动钻削的运动学特性及其对加工质量的影响[J]. 中国机械工程,2009,20(17):2027-2031. MA Lijie,WANG Xibin,LIU Guanjun,et al. Kinematics characteristics of vibration drilling and its influence on processing quality[J]. China Mechanical Engineering,2009,20(17):2027-2031.
[26] 史中权,叶文华. 多轴联动条件下插补速度实时可调的前瞻控制算法[J]. 航空学报,2014,35(2):582-592. SHI Zhongquan,YE Wenhua. A look-ahead algorithm with adjustable real-time feedrate based on multi-axis synchronous interpolation[J]. Acta Aeronautica et Astronautica Sinica,2014,35(2):582-592.
[27] QU S,ZHAO J B,WANG T. Three-dimensional stability prediction and chatter analysis in milling of thin-walled plate[J]. The International Journal of Advanced Manufacturing Technology,2016,86:2291-2300.