Research Papers

A New Surface Topography-Based Method to Quantify Axial Error of High Speed Milling Cutters

[+] Author and Article Information
Wanqun Chen

Centre for Precision Technologies,
University of Huddersfield,
Huddersfield HD1 3DH, UK;
School of Mechatronics Engineering,
Harbin Institute of Technology,
Harbin 150001, China;
Haslett Building (HA3/05) EPSRC
Future Metrology Hub,
University of Huddersfield,
Huddersfield HD1 3DH, UK
e-mail: Wanqun.chen@ncl.ac.uk

Lei Lu

School of Mechatronics Engineering,
Harbin Institute of Technology,
No.92 West Dazhi Street,
Harbin 150001, China
e-mail: lulei_71@163.com

Wenkun Xie

Centre of Micro/Nano Manufacturing
Technology (MNMT-Dublin),
University College Dublin,
Dublin 4, Ireland
e-mail: wenkun.xie@ucd.ie

Dehong Huo

Mechanical Engineering,
School of Engineering, Newcastle University,
Newcastle upon Tyne, NE1 7RU, UK
e-mails: Dehong.huo@ncl.ac.uk;

Kai Yang

School of Mechatronics Engineering,
Army Aviation Institute,
No.42 Tongzhou Street,
Beijing 101123, China
e-mail: Kaiyang4545@163.com

1Corresponding author.

Manuscript received March 27, 2018; final manuscript received August 6, 2018; published online August 31, 2018. Assoc. Editor: Guillaume Fromentin.

J. Manuf. Sci. Eng 140(11), 111014 (Aug 31, 2018) (9 pages) Paper No: MANU-18-1190; doi: 10.1115/1.4041180 History: Received March 27, 2018; Revised August 06, 2018

Cutting tool rotation errors have significant influence on the machined surface quality, especially in micromilling. Precision metrology instruments are usually needed to measure the rotation error accurately. However, it is difficult to directly measure the axial error of micromilling tools due to the small diameters and ultra-high rotational speed. To predict the axial error of high speed milling tools in the actual machining conditions and avoid the use of expensive metrology instruments, a novel method is proposed in this paper to quantify the cutting tool error in the axial direction based on the tool marks generated on the machined surface. A numerical model is established to simulate the surface topography generation, and the relationship between tool marks and the cutting tool axial error is then investigated. The tool axial errors at different rotational speeds can be detected by the proposed method. The accuracy and the reliability of the proposed method are verified by machining experiments.

Copyright © 2018 by ASME
Your Session has timed out. Please sign back in to continue.


Chen, D. , Fan, J. , and Zhang, F. , 2012, “ An Identification Method for Spindle Rotation Error of a Diamond Turning Machine Based on the Wavelet Transform,” Int. J. Adv. Manuf. Technol., 63(5–8), pp. 457–464. [CrossRef]
Zhang, G. X. , and Wang, R. K. , 1993, “ Four-Point Method of Roundness and Spindle Error Measurements,” CIRP Ann.-Manuf. Technol., 42(1), pp. 593–596. [CrossRef]
Shu, Q. , Zhu, M. , Liu, X. , and Cheng, H. , 2017, “ Radial Error Motion Measurement of Ultraprecision Axes of Rotation With Nanometer Level Precision,” ASME J. Manuf. Sci. Eng., 139(7), p. 071017. [CrossRef]
Grejda, R. , Marsh, E. , and Vallance, R. , 2005, “ Techniques for Calibrating Spindles With Nanometer Error Motion,” Precis. Eng., 29(1), pp. 113–123. [CrossRef]
Chen, N. , Chen, M. J. , Wu, C. Y. , Pei, X. D. , Qian, J. , and Reynaerts, D. , 2017, “ Research in Minimum Undeformed Chip Thickness and Size Effect in Micro End-Milling of Potassium Dihydrogen Phosphate Crystal,” Int. J. Mech. Sci., 134, pp. 387–398. [CrossRef]
Zhou, Y. , Chen, Z. C. , and Tang, J. , 2017, “ A New Method of Designing the Tooth Surfaces of Spiral Bevel Gears With Ruled Surface for Their Accurate Five-Axis Flank Milling,” ASME J. Manuf. Sci. Eng., 139(6), p. 061004. [CrossRef]
Schmitz, T. L. , Couey, J. , Eric, M. , Mauntler, N. , and Hughes, D. , 2007, “ Runout Effects in Milling: Surface Finish, Surface Location Error, and Stability,” Int. J. Mach. Tools Manuf., 47(5), pp. 841–851. [CrossRef]
Chen, W. Q. , Huo, D. H. , Teng, X. Y. , and Sun, Y. Z. , 2017, “ Surface Generation Modelling for Micro End Milling Considering the Minimum Chip Thickness and Tool Runout,” Procedia CIRP, 58, pp. 364–369. [CrossRef]
Kanlı, M. , 2014, Modeling of Cutting Forces in Micro Milling Including Run-out [D], Bilkent University, Bilkent, Ankara, Turkey.
Zhang, X. , Ehmann, K. F. , Yu, T. , and Wang, W. , 2016, “ Cutting Forces in Micro-End-Milling Processes,” Int. J. Mach. Tools Manuf., 107, pp. 21–40. [CrossRef]
Zhang, X. , Zhang, J. , Zhang, W. , Li, J. , and Zhao, W. , 2018, “ A Non-Contact Calibration Method for Cutter Runout With Spindle Speed Dependent Effect and Analysis of Its Influence on Milling Process,” Precis. Eng., 51, pp. 280–290. [CrossRef]
Lee, C. , Zhao, R. , and Jeon, S. , 2017, “ A Simple Optical System for Miniature Spindle Runout Monitoring,” Measurements, 102, pp. 42–46.
Nakkiew, W. , Lin, C. W. , and Tu, J. F. , 2006, “ A New Method to Quantify Radial Error of a Motorized End-Milling Cutter System at Very High Speed Rotations,” Int. J. Mach. Tools Manuf., 46(7-8), pp. 877–889. [CrossRef]
Attanasio, A. , and Ceretti, E. , 2018, “ Experimental Evaluation of Tool Run-out in Micro Milling,” AIP Conf. Proc., 1960(1), p. 070002.
Ji, W. , Shi, J. , Liu, X. , Wang, L. , and Liang, S. Y. , 2017, “ A Novel Approach of Tool Wear Evaluation,” ASME J. Manuf. Sci. Eng., 139(9), p. 091015. [CrossRef]
Krüger, M. , and Denkena, B. , 2013, “ Model-Based Identification of Tool Runout in End Milling and Estimation of Surface Roughness From Measured Cutting Forces,” Int. J. Adv. Manuf. Technol., 65(5–8), pp. 1067–1080. [CrossRef]
Seethaler, R. J. , and Yellowley, I. , 1999, “ The Identification of Radial Runout in Milling Operations,” ASME J. Manuf. Sci. Eng., 121(3), pp. 524–531. [CrossRef]
Jing, X. , Tian, Y. , Yuan, Y. , and Wang, F. , 2017, “ A Runout Measuring Method Using Modeling and Simulation Cutting Force in Micro End-Milling,” Int. J. Adv. Manuf. Technol., 91(9–12), pp. 4191–4201. [CrossRef]
Bissacco, G. , Hans, N. H. , and Slunsky, J. , 2008, “ Modelling the Cutting Edge Radius Size Effect for Force Prediction in Micro Milling,” CIRP Ann.-Manuf. Technol., 57(1), pp. 113–116. [CrossRef]
Li, H. , and Wu, B. , 2016, “ Development of a Hybrid Cutting Force Model for Micromilling of Brass,” Int. J. Mech. Sci., 115–116, pp. 586–595. [CrossRef]
Vogler, M. P. , Shiv, G. K. , and Richard, E. D. , 2004, “ On the Modeling and Analysis of Machining Performance in Micro-Endmilling—Part II: Cutting Force Prediction,” ASME J. Manuf. Sci. Eng., 126(4), pp. 695–705. [CrossRef]
Huang, N. E. , Shen, Z. , Long, S. L. , Wu, M. C. , Shih, H. H. , Zheng, Q. , Yen, N.-C. , Tung, C. C. , and Liu, H. H. , 1988, “ The Empirical Mode Decomposition and the Hilbert Spectrum for Nonlinear and Non-Stationary Time Series Analysis,” Proc. R. Soc. London A: Math., Phys. Eng. Sci., 454, pp. 903–995.
Zhang, B. , Zhang, C. , and Wu, J. , 2014, “ A Medical Image Fusion Method Based on Energy Classification of BEMD Components,” Optik - Int. J. Light Electron. Opt., 125(1), pp. 146–153. [CrossRef]
Acharya, U. R. , Mookiah, M. R. K. , and Koh, J. E. W. , 2016, “ Automated Screening System for Retinal Health Using Bi-Dimensional Empirical Mode Decomposition and Integrated Index,” Comput. Biol. Med., 75, pp. 54–62. [CrossRef] [PubMed]


Grahic Jump Location
Fig. 1

Cutter error in micro milling: (a) schematic of runout in milling process, (b) no radial runout, (c) radial runout, (d) no axial runout, and (e) axial runout

Grahic Jump Location
Fig. 2

Tool runout measurements: (a) static measurement, (b) dynamic measurement, (c) tool marks measurement, and (d) cutting force measurement

Grahic Jump Location
Fig. 3

The tool path diagram considering runout: (a) radial runout and (b) axial runout

Grahic Jump Location
Fig. 4

Tool geometry: (a) side view, (b) top view, and (c) end cutter edge profile

Grahic Jump Location
Fig. 8

Prediction process of the axial runout

Grahic Jump Location
Fig. 7

The flow chart of the axial runout detection process

Grahic Jump Location
Fig. 6

Axial runout judgment diagram

Grahic Jump Location
Fig. 13

Axial runout test setup: (a) experiment setup and (b) schematic setup

Grahic Jump Location
Fig. 5

Schematic diagram of the machined surface: (a) ideal surface, (b) surface type 1 with axial runout, and (c) surface type 2 with axial runout

Grahic Jump Location
Fig. 9

Machined surface data decomposed by BEMD method (feed per tooth: 8 μm, spindle speed: 35,000 rpm)

Grahic Jump Location
Fig. 10

PSD analysis of IMFs (feed per tooth: 8 μm, spindle speed: 35,000 rpm)

Grahic Jump Location
Fig. 11

Machined surface decomposed by BEMD method (feed per tooth: 12 μm, spindle speed: 35,000 rpm)

Grahic Jump Location
Fig. 12

PSD analysis of IMFs (Feeding parameter larger than threshold)



Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In