Research Papers

On-Line Energy-Based Milling Chatter Detection

[+] Author and Article Information
Hakan Caliskan

Postdoctoral Researcher,
Manufacturing Automation Laboratory,
Department of Mechanical Engineering,
The University of British Columbia,
Vancouver, BC V6T 1Z4, Canada
e-mail: chakan@metu.edu.tr

Zekai Murat Kilic

Postdoctoral Researcher,
Manufacturing Automation Laboratory, Department of Mechanical Engineering,
The University of British Columbia,
Vancouver, BC V6T 1Z4, Canada
e-mail: zmuratk@mail.ubc.ca

Yusuf Altintas

Fellow ASME
Manufacturing Automation Laboratory,
Department of Mechanical Engineering,
The University of British Columbia,
Vancouver, BC V6T 1Z4, Canada
e-mail: altintas@mail.ubc.edu

1Instructor at Mechanical Engineering Department, Middle East Technical University, Turkey.

2Corresponding author.

Manuscript received February 12, 2018; final manuscript received June 13, 2018; published online August 31, 2018. Assoc. Editor: Satish Bukkapatnam.

J. Manuf. Sci. Eng 140(11), 111012 (Aug 31, 2018) (12 pages) Paper No: MANU-18-1085; doi: 10.1115/1.4040617 History: Received February 12, 2018; Revised June 13, 2018

Milling exhibits forced vibrations at tooth passing frequency and its harmonics, as well as chatter vibrations close to one of the natural modes. In addition, there are sidebands, which are spread at the multiples of tooth passing frequency above and below the chatter frequency, and make the robust chatter detection difficult. This paper presents a novel on-line chatter detection method by monitoring the vibration energy. Forced vibrations are removed from the measurements in discrete time domain using a Kalman filter. After removing all periodic components, the amplitude and frequency of chatter are searched in between the two consecutive tooth passing frequency harmonics using a nonlinear energy operator (NEO). When the energy of any chatter component grows relative to the energy of forced vibrations, the presence of chatter is detected. The proposed method works in discrete real time intervals, and can detect the chatter earlier than frequency domain-based methods, which rely on fast Fourier Transforms. The method has been experimentally validated in several milling tests using both microphone and accelerometer measurements, as well as using spindle speed and current signals.

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Grahic Jump Location
Fig. 2

Chatter frequency and its side bands

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Fig. 3

Proposed methodology

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Fig. 4

Experimental setup

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Fig. 5

Case 1: Separation of periodic and chatter components of measured acceleration signals. Cutting conditions: Tool diameter: 10 mm with 2 flutes, spindle speed: 12,000 rev/min, feed: 1800 mm/min (0.075 mm/tooth), axial depth of cut: 14 mm, the radial depth of cut increases linearly from 1.5 to 3 mm. Dashed lines show tooth passing frequency harmonics (ωt = 400 Hz) while the dotted lines show spindle frequency harmonics (ωs = 200 Hz): (a) Measured acceleration signal, (b) stable zone and chatter onset zone, (c) FFT in between [1.886, 1.916] s and [2.391, 2.421] s, and (d) FFT in between 1.886–1.916 s and 2.391–2.421 s.

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Fig. 6

Case 1: Bank of band-pass filters

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Fig. 7

Case 1: Chatter signals isolated from the measurements. Estimated chatter amplitudes Sc,m and frequencies ωCH,m from the chatter signal sc(t) for tooth passing harmonic intervals m = 5, 6, 7, 8. FFTs are used to validate the algorithm. Cutting conditions are given in Fig. 5: (a) acceleration, (b) estimated chatter parameters, (c) energy of periodic and chatter component, and (d) FFT at different time intervals.

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Fig. 8

Case 1: Comparison of microphone and acceleration for chatter detection. Cutting conditions are given in Fig. 5: (a) microphone and (b) acceleration.

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Fig. 9

Case 2: Measured microphone signals from stable and unstable milling experiments. Cutting conditions: Down milling of Aluminum AL7050-T7451, tool diameter: 19.05 mm with 4 flutes, spindle speeds: 10,000 rev/min, tooth passing frequency ωt = 667 Hz, feed: 10,000 mm/min (0.250 mm/tooth). Radial depth of cut is 0.953 mm, axial depth of cut is 20 mm for the stable (a) and 25 mm for the unstable (b) cuts: (a) stable microphone, (b) unstable microphone, (c) FFT, [0.1–0.2] s and (d) FFT, [0.1–0.2] s.

Grahic Jump Location
Fig. 10

Case 3: Comparison of microphone and acceleration signals, and spindle speed and motor current set-point signals with mean values removed. Cutting conditions: Down milling of Aluminum AL7050-T7451, tool diameter: 20 mm with 2 inserts, spindle speed: 9000 rev/min, feed: 2000 mm/min (0.111 mm/tooth), axial depth of cut: 0.8 mm, the radial depth of cut increases from 0 to 20 mm linearly in 150 mm tool path: (a) microphone, (b) acceleration, (c) spindle speed, and (d) spindle current.



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