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Research Papers

Adaptive Active Chatter Control in Milling Processes

[+] Author and Article Information
Zhiyong Chen

School of Electrical Engineering and
Computer Science,
University of Newcastle,
Callaghan NSW 2308, Australia
e-mail: zhiyong.chen@newcastle.edu.au

Hai-Tao Zhang

State Key Laboratory of Digital Manufacturing
Equipment and Technology,
Huazhong University of Science and Technology,
Wuhan 430074, China
e-mail: zht@mail.hust.edu.cn

Xiaoming Zhang

State Key Laboratory of Digital Manufacturing
Equipment and Technology,
Huazhong University of Science and Technology,
Wuhan 430074, China
e-mail: cheungxm@mail.hust.edu.cn

Han Ding

State Key Laboratory of Digital Manufacturing
Equipment and Technology,
Huazhong University of Science and Technology,
Wuhan 430074, China
e-mail: dinghan@mail.hust.edu.cn

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received February 11, 2013; final manuscript received October 10, 2013; published online December 2, 2013. Assoc. Editor: Prashant Mehta.

J. Dyn. Sys., Meas., Control 136(2), 021007 (Dec 02, 2013) (7 pages) Paper No: DS-13-1070; doi: 10.1115/1.4025694 History: Received February 11, 2013; Revised October 10, 2013

Chatter is an undesirable dynamic phenomenon in machining processes, which causes cutting disturbance, overcut, quick tool wear, etc., and thus seriously impairs workpiece quality. To mitigate chatter, traditional methods called passive control focus on optimizing working spindle speeds and depths of cut. But they have inherent disadvantages in gaining highly efficient machining. On the contrary, the research in this paper is along the line of active control. Specifically, an adaptive algorithm is developed based on Fourier series analysis to deal with the so-called regenerative cutting force which causes chatter. As a result, chatter is remarkably mitigated. The performance improvement is illustrated by numerical simulation in terms of both stability lobes diagram (SLD) and surface location error (SLE).

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References

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Figures

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Fig. 1

Schematic diagram of the milling process equipped with active control structures. Left: top view; right: side view.

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Fig. 2

Illustration of the cutting force F = [Fx,Fy]T for the cutting tool with two evenly spaced teeth

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Fig. 3

Block diagram of the milling process with active control

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Fig. 8

The tooth trajectories of (4) for b = 1 mm and Ω = 2500 rpm under the controller (10). Dotted line: nominal circle with radius of R = 2.5 mm, solid line: the trajectory of tooth 1; dashed line: the trajectory of tooth 2.

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Fig. 4

SLBs for the open-loop system (5) with u = 0 and the closed-loop system with the proposed controller (10). Specifically, the system working with the parameters b and Ω below the curve is stable.

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Fig. 5

The perturbation system (5) is unstable for b = 1mm and Ω = 2500 rpm without active control

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Fig. 6

The perturbation system (5) is stable for b = 1 mm and Ω=2500 rpm under the controller (10) for ℓ=0,1,2

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Fig. 7

The tool displacements of (4) for b = 1 mm and Ω=2500 rpm under the controller (10)

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