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Research Papers

Kinematic Analysis and Dimensional Synthesis of Exechon Parallel Kinematic Machine for Large Volume Machining

[+] Author and Article Information
Y. Jin

School of Mechanical & Aerospace Engineering,
Queen's University Belfast,
Belfast BT9 5AH, UK
e-mail: y.jin@qub.ac.uk

Z. M. Bi

Department of Engineering,
Indiana University Purdue University,
Fort Wayne, IN 46805
e-mail: biz@ipfw.edu

H. T. Liu

School of Mechanical Engineering,
Tianjin University,
Tianjin 300072, China
e-mail: liuhaitao_tju@126.com

C. Higgins

Northern Ireland Technology Centre,
Queen's University Belfast,
Belfast BT9 5AH, UK
e-mail: c.j.higgins@qub.ac.uk

M. Price

School of Mechanical & Aerospace Engineering,
Queen's University Belfast,
Belfast BT9 5AH, UK
e-mail: m.price@qub.ac.uk

W. H. Chen

School of Automation Science
and Electrical Engineering,
Beihang University,
Beijing 100191, China
e-mail: whchen@buaa.edu.cn

T. Huang

School of Mechanical Engineering,
Tianjin University,
Tianjin 300072, China
e-mail: tianhuang@tju.edu.cn

1Corresponding author.

Contributed by the Mechanisms and Robotics Committee of ASME for publication in the JOURNAL OF MECHANISMS AND ROBOTICS. Manuscript received August 5, 2013; final manuscript received December 18, 2014; published online March 11, 2015. Assoc. Editor: Xianmin Zhang.

J. Mechanisms Robotics 7(4), 041004 (Nov 01, 2015) (8 pages) Paper No: JMR-13-1150; doi: 10.1115/1.4029499 History: Received August 05, 2013; Revised December 18, 2014; Online March 11, 2015

A parallel kinematic machine (PKM) topology can only give its best performance when its geometrical parameters are optimized. In this paper, dimensional synthesis of a newly developed PKM is presented for the first time. An optimization method is developed with the objective to maximize both workspace volume and global dexterity of the PKM. Results show that the method can effectively identify design parameter changes under different weighted objectives. The PKM with optimized dimensions has a large workspace to footprint ratio and a large well-conditioned workspace, hence justifies its suitability for large volume machining.

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Figures

Grahic Jump Location
Fig. 1

Physical model of a 5DOF hybrid Exechon machine in QUB

Grahic Jump Location
Fig. 2

Schematic diagram of Exechon-PKM

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Fig. 4

Reachable workspace of the three legs

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Fig. 5

Optimization procedure of Exechon-PKM

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Fig. 6

Optimization results of Exechon-PKM

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Fig. 7

Workspace of the Exechon-PKM

Grahic Jump Location
Fig. 8

Conditioning distribution of workspace. (a) z = 0.85, (b) z = 0.9, (c) z = 0.95, (d) z = 1.0, (e) z = 1.05, and (f) z = 1.1.

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