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Research Papers

Robust Active Chatter Control in Milling Processes With Variable Pitch Cutters

[+] Author and Article Information
Tao Huang

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

Lijun Zhu

Department of Electrical and Electronic
Engineering,
The University of Hong Kong,
Hong Kong, China
e-mail: ljzhu@eee.hku.hk

Shengli Du

College of Automation,
Faculty of Information Technology,
Beijing University of Technology,
Beijing 100124, China
e-mail: shenglidu@bjut.edu.cn

Zhiyong Chen

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

Han Ding

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

1Corresponding author.

Manuscript received December 27, 2017; final manuscript received June 15, 2018; published online July 9, 2018. Assoc. Editor: Satish Bukkapatnam.

J. Manuf. Sci. Eng 140(10), 101005 (Jul 09, 2018) (9 pages) Paper No: MANU-17-1812; doi: 10.1115/1.4040618 History: Received December 27, 2017; Revised June 15, 2018

Milling chatters caused by the regenerative effect is one of the major limitations in increasing the machining efficiency and accuracy of milling operations. This paper studies robust active chatter control for milling processes with variable pitch cutters whose dynamics are governed by multidelay nonlinear differential equations. We propose a state feedback controller based on linear matrix inequality (LMI) approach that can enlarge multiple stability domains in the stability lobe diagram (SLD) while the controller gain is minimized. Numerical simulations of active magnetic bearing systems demonstrate the effectiveness of the proposed method.

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References

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Figures

Grahic Jump Location
Fig. 2

(a) SLD of the open-loop system, (b) SLD of the closed-loop system with L = L1 for low-speed operation enhancement, (c) SLD of the closed-loop system with L = L2 for high-speed operation enhancement, and (d) SLD of the closed-loop system with L = L3 for multiple-zone operation enhancement. Specifically, the system working with the parameters b and Ω below the curve is stable. The areas highlighted in gray are predesignated target stability domains.

Grahic Jump Location
Fig. 1

The schematic diagram of the milling process with a three-flutes cutter

Grahic Jump Location
Fig. 3

Time domain simulation performed for Eq. (6) with Ω = 4000 rpm and b = 0.4 mm. (Upper) control off. (Middle upper) cancelation control (12) on. (Middle lower) feedback control (7) on with L = L1. (Lower) current input signal i = col(ix, iy) for robust active control.

Grahic Jump Location
Fig. 4

Time domain simulation performed for Eq. (6) with Ω = 11,000 rpm and b = 0.25 mm. (Upper) control off. (Middle upper) cancelation control (12) on. (Middle lower) feedback control (7) on with L = L2. (Lower) current input signal i = col(ix, iy) for robust active control.

Grahic Jump Location
Fig. 5

Upper: the milling process is performed at low-speed operation with Ω = 3700 rpm and b = 0.4 mm. Middle: the milling process is performed at midspeed operation with Ω = 6000 rpm and b = 0.4 mm. Lower: the milling process is performed at high-speed operation with Ω = 11,000 rpm and b = 0.25 mm.

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