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Research Papers

A Model-Based Computationally Efficient Method for On-Line Detection of Chatter in Milling

[+] Author and Article Information
Shreyes N. Melkote

George W. Woodruff School of Mechanical Engineering,
Georgia Institute of Technology,
Atlanta, GA 30332

James B. Castle

The Boeing Company,
Boeing Research and Technology,
St. Louis, MO 63166

Contributed by the Manufacturing Engineering Division of ASME for publication in the Journal of Manufacturing Science and Engineering. Manuscript received April 2, 2012; final manuscript received October 29, 2012; published online May 24, 2013. Assoc. Editor: Tony Schmitz.

J. Manuf. Sci. Eng 135(3), 031007 (May 24, 2013) (11 pages) Paper No: MANU-12-1101; doi: 10.1115/1.4023716 History: Received April 02, 2012; Revised October 29, 2012

This paper presents a model-based computationally efficient method for detecting milling chatter in its incipient stages and for chatter frequency estimation by monitoring the cutting force signals. Based on a complex exponentials model for the dynamic chip thickness, the chip regeneration effect is amplified and isolated from the cutting force signal for early chatter detection. The proposed method is independent of the cutting conditions. With the aid of a one tap adaptive filter, the method is shown to be capable of distinguishing between chatter and the dynamic transients in the cutting forces arising from sudden changes in workpiece geometry and tool entry/exit. To facilitate chatter suppression once the onset of chatter is detected, a time domain algorithm is proposed so that the dominant chatter frequency can be accurately determined without using computationally expensive frequency domain transforms such as the Fourier transform. The proposed method is experimentally validated.

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Figures

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Fig. 1

Chip regeneration in milling

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Fig. 2

Ideal differentiator and its finite order approximations

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Fig. 3

Global trend and local variation in xk

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Fig. 4

Relationship between the pole location and the spectrum of the signal

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Fig. 5

Flow chart of the chatter detection and chatter frequency estimation method

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Fig. 6

Inputs and outputs of the chatter detection algorithms for Test 1 (a) (four flute 6.35 mm carbide tool, 1018 steel workpiece, 3400 rpm, 0.0254 mm feed/tooth, 50% radial immersion, 2.54 mm depth of cut at beginning)

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Fig. 7

Inputs and outputs of the chatter detection algorithms for Test 1 (b)

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Fig. 8

Inputs and outputs of the chatter detection algorithms for Test 2 (a) (two flute 25.4 mm carbide tool, 1018 steel workpiece, 1200 rpm, 0.0381 mm feed/tooth, 25% radial immersion, 2.54 mm axial depth of cut)

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Fig. 9

Inputs and outputs of the chatter detection algorithms for Test 2 (b)

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Fig. 10

Workpiece geometries and toolpaths for cutting Tests 3 and 4

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Fig. 11

Inputs and outputs of the chatter detection algorithms for Test 3 (a) (two flute 12.7 mm carbide tool, aluminum 7050 workpiece, 2400 rpm, 50%–100% radial immersion, 2.54 mm axial depth of cut, 0.016 mm feed/tooth)

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Fig. 12

Inputs and outputs of the chatter detection algorithms for Test 3 (b)

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Fig. 13

Inputs and outputs of the chatter detection algorithms for Test 4 (a) (two flute 12.7 mm carbide tool, aluminum 7050 workpiece, 2400 rpm, 50%–100% radial immersion, 1.905 mm axial depth of cut, 0.0254 mm feed/tooth)

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Fig. 14

Inputs and outputs of the chatter detection algorithms for Test 4 (b)

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Fig. 15

Workpiece geometry and toolpath for cutting Test 5

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Fig. 16

Inputs and outputs of the chatter detection algorithms for Test 5 (a) (four flute 6.35 mm high speed steel tool, aluminum 7050 workpiece, 2400 rpm, 50% radial immersion, 3.81 mm–11.43 mm axial depth of cut, 0.0381 mm feed/tooth)

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Fig. 17

Inputs and outputs of the chatter detection algorithms for Test 5 (b)

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Fig. 18

Comparison of the FFT of f(t) (top) and F(t) (bottom)

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Fig. 19

Dominant chatter frequency estimated by the proposed algorithm (top) and the FFT (bottom) (data is from Test 1)

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Fig. 20

Dominant chatter frequency estimated by the proposed algorithm (top) and the FFT (bottom) (data is from Test 2)

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