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Research Papers

Automatic In-Process Chatter Avoidance in the High-Speed Milling Process

[+] Author and Article Information
N. J. M. van Dijk1

Department of Mechanical Engineering, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, The Netherlandsn.j.m.v.dijk@tue.nl

E. J. J. Doppenberg, J. A. J. Oosterling

 TNO Science and Industry, P.O. Box 6235, 5600 HE Eindhoven, The Netherlands

R. P. H. Faassen

 TMC Mechatronics. P.O. Box 700, 5600 AS Eindhoven, The Netherlands

N. van de Wouw, H. Nijmeijer

Department of Mechanical Engineering, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, The Netherlands

Note that the minimization of ε2 in Jp(θp)=E[ε2(k,θp)] is only an intermediate step in the estimation process.

1

Corresponding author.

J. Dyn. Sys., Meas., Control 132(3), 031006 (Apr 21, 2010) (14 pages) doi:10.1115/1.4000821 History: Received April 15, 2009; Revised November 23, 2009; Published April 21, 2010; Online April 21, 2010

High-speed milling is often used in industry to maximize productivity of the manufacturing of high-technology components, such as aeronautical components, mold, and dies. The occurrence of chatter highly limits the efficiency and accuracy of high-speed milling operations. In this paper, two control strategies are presented that guarantee a chatter-free high-speed milling operation by automatic adaptation of spindle speed and feed. Moreover, the proposed strategies are robust for changing process conditions (e.g., due to heating of the spindle or tool wear). An important part of the control strategy is the detection of chatter. A novel chatter detection algorithm is presented that automatically detects chatter in an online fashion and in a premature phase such that no visible marks on the workpiece are present. Experiments on a state-of-the-art high-speed milling machine underline the effectiveness of the proposed detection and control strategies.

Copyright © 2010 by American Society of Mechanical Engineers
Topics: Chatter , Milling
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References

Figures

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Figure 2

Block diagram of the milling process

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Figure 8

Schematic overview of the adaptive chatter controller

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Figure 9

Experimental results of chatter detection method. Chatter is first detected after 288 mm.

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Figure 10

Spectrogram of the measured acceleration at the lower spindle bearing for a cut without control. The brighter colors represent a larger magnitude of the frequency component.

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Figure 11

Detail of the workpiece for a single cut without control at a spindle speed of 35,000 rpm with an increasing depth of cut from 2.0 mm to 3.0 mm

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Figure 12

Experimental results of the control strategies for a cut at 35000 rpm with increasing ap from 2.0 mm to 3.0 mm in 2.6 s. (Left figures) Control strategy 1. (Right figures) Control strategy 2.

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Figure 13

Spectrogram of the acceleration measured at the lower spindle bearing for both control strategies

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Figure 14

Results of the experiments with the controllers switched on

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Figure 15

Detail of the workpiece with and without chatter control. The depth of cut is increasing from 2.0 mm to 3.0 mm and the spindle speed is 35,000 rpm.

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Figure 1

Schematic representation of the milling process

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Figure 5

Detection using various sensors

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Figure 6

Schematic representation of the closed loop including the milling process, chatter detection, and chatter control

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Figure 7

Power spectral density H(fchat(k),θu) (solid) and Δf(k) (dashed) based on measured accelerations for a spindle-speed sweep from 29,600 rpm to 38,600 rpm at a constant depth of cut

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Figure 3

The setup: (1) microphone, (2) accelerometers, (3) eddy current sensors, (4) dynamometer, (5) tool, (6) workpiece, (7) mounting device, (8) spindle, (9) toolholder, and (10) bed

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Figure 4

Power spectral density of the measured acceleration in the y-direction in the case of chatter

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