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Research Papers

A Method for Mode Coupling Chatter Detection and Suppression in Robotic Milling

[+] Author and Article Information
Lejun Cen

George W. Woodruff School of
Mechanical Engineering,
Georgia Institute of Technology,
Atlanta, GA 30332-0405

Shreyes N. Melkote

George W. Woodruff School of
Mechanical Engineering,
Georgia Institute of Technology,
Atlanta, GA 30332-0405
e-mail: shreyes.melkote@me.gatech.edu

James Castle

MR&D,
Boeing Research and Technology,
St. Louis, MO 63166

Howard Appelman

Metals Technology Integration,
Boeing Research and Technology,
St. Louis, MO 63166

1Corresponding author.

Manuscript received January 17, 2018; final manuscript received April 16, 2018; published online June 4, 2018. Editor: Y. Lawrence Yao.

J. Manuf. Sci. Eng 140(8), 081015 (Jun 04, 2018) (9 pages) Paper No: MANU-18-1039; doi: 10.1115/1.4040161 History: Received January 17, 2018; Revised April 16, 2018

A new method for online chatter detection and suppression in robotic milling is presented. To compute the chatter stability of robotic milling along a curvilinear tool path characterized by significant variation in robot arm configuration and cutting conditions, the tool path is partitioned into small sections such that the dynamic stability characteristics of the robot can be assumed to be constant within each section. A methodology to determine the appropriate section length is proposed. The instantaneous cutting force-induced dynamic strain signal is measured using a wireless piezoelectric thin-film polymer (polyvinyldene fluoride (PVDF))-based sensor system, and a discrete wavelet transform (DWT)-based online chatter detection algorithm and chatter suppression strategy are developed and experimentally evaluated. The proposed chatter detection algorithm is shown to be capable of recognizing the onset of chatter while the chatter suppression strategy is found to be effective in minimizing chatter during robotic milling.

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Figures

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Fig. 1

Flow chart of methodology

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Fig. 2

Equivalent two-dimensional model for robotic milling chatter analysis (see Ref. [20] for details)

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Fig. 3

Partitioning of the tool path into sections

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Fig. 4

(a) Δγmax for case 1 (positive slope) and (b) Δγmax for case 2 (negative slope)

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Fig. 5

Calculating Δγ using linear approximation

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Fig. 6

Experimental setup for closed-loop robotic milling

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Fig. 7

Tool path first starts in the X direction and then moves in the Y direction. Figures 4(a) and 4(b) represent the stability characteristics for the tool path sections boxed in red and green, respectively.

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Fig. 8

Effect of different safety factors on the resultant force measured in tests #1 and #2

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Fig. 9

Open-loop robotic milling in the X and Y directions (tests #1 and #2)

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Fig. 10

Closed-loop robotic milling in the X and Y directions (tests #3 and #4)

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Fig. 11

Open-loop robotic milling in the X and Y directions (tests #5 and #6)

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Fig. 12

Closed-loop robotic milling in the X and Y directions (tests #7 and #8)

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