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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;
dehong.huo@newcastle.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.

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Figures

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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

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Fig. 2

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

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Fig. 3

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

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Fig. 4

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

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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

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Fig. 6

Axial runout judgment diagram

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Fig. 7

The flow chart of the axial runout detection process

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Fig. 8

Prediction process of the axial runout

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Fig. 9

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

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Fig. 10

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

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Fig. 11

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

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Fig. 12

PSD analysis of IMFs (Feeding parameter larger than threshold)

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Fig. 13

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

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