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Research Papers

Flexure Parallel Mechanism: Configuration and Performance Improvement of a Compact Acceleration Sensor

[+] Author and Article Information
Zhen Gao

Faculty of Engineering and Applied Science,  University of Ontario Institute of Technology, Oshawa, ON, L1H 7K4, Canada

Dan Zhang1

Faculty of Engineering and Applied Science,  University of Ontario Institute of Technology, Oshawa, ON, L1H 7K4, Canadadan.zhang@uoit.ca

1

Corresponding author.

J. Mechanisms Robotics 4(3), 031002 (May 07, 2012) (10 pages) doi:10.1115/1.4006660 History: Received September 26, 2011; Revised March 30, 2012; Published May 07, 2012; Online May 07, 2012

This research presents a tridimensional acceleration sensor based on flexure parallel mechanism (FPM). Three perpendicular compliant limbs with compact monolithic structure are developed to serve as the elastic component for acquiring the inertial signals in each direction. With integrated flexure hinges, each chain containing multiple revolute joints and cantilever beams are designed to carry compressive and tensile loads. First, the structure evolution and kinematics modeling are introduced, followed by the multispring modeling of the directional compliance for the flexure limb. Then, the comprehensive finite-element analysis (FEA) including the strain of the sensitive limbs, modal analysis for total deformation under different frequency is conducted. The compliances calculated by FEA and multispring model are compared. Finally, the dimensional optimization is implemented based on multipopulation genetic algorithm to obtain the optimal flexure parameters. The proposed methods and algorithms are also useful for the analysis and development of other flexure parallel mechanisms.

Copyright © 2012 by American Society of Mechanical Engineers
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Figures

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Figure 1

Different positions for the applied forces on the moving stage produce different output matrix

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Figure 2

The prototype of the multidimensional accelerometer

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Figure 3

The proposed accelerometer based on compact FPM; (a) without base and cuboids and (b) with base and cuboids

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Figure 4

Kinematic modeling of the accelerometer based on compact FPM

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Figure 5

Compliant elastic limb, (a) CAD model, (b) dimensional sketch, and (c) mesh model

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Figure 6

The multispring model of the directional compliance for the flexure limb

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Figure 7

The meshing representation

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Figure 8

The strain results under different inputs of acceleration in single-direction; (a) and (b), when a = 2G, the maximal strain is 3.139 × 10−6 mm, the minimal strain is 2.48 × 10−10 mm, the maximal total deformation is 2.227 × 10−4 mm; (c) and (d), when a= 4G, the maximal strain is 6.278 × 10−6 mm, the minimal strain is 4.96 × 10−10 mm, the maximal total deformation is 4.453 × 10−4 mm; (e) and (f), when a= 6G, the maximal strain is 9.416 × 10−6 mm, the minimal strain is 7.74 × 10−10 mm, the maximal total deformation is 6.68 × 10−4 mm; (g) and (h), when a= 8G, the maximal strain is 1.256 × 10−5 mm, the minimal strain is 9.92 × 10−10 mm, the maximal total deformation is 8.907 × 10−4 mm

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Figure 9

Compliance comparison between multispring theory and FEA result

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Figure 10

Modal analysis for total deformation under different frequency input; (a) first modal with the resonance frequency 1745.9, (b) second modal with the resonance frequency 1758.1, (c) third modal with the resonance frequency 1762.7, (d) fourth modal with the resonance frequency 1814

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Figure 11

Schematic representation of the optimization rationale based on multipopulation genetic algorithms

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Figure 12

The comparisons of compliant parameters before and after optimization

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Figure 13

The comparisons of individuals’ number in each subpopulation before and after optimization

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Figure 14

The evolution of dimensional parameters of the flexure joints, (a) the optimal objection value in each subpopulation, (b) the evolved values of the nine dimensional parameters that are obtained by the evolution of the five subpopulations, (c) individual number of each subpopulation in the evolutionary process, (d) the 85% best objective values of all generations, (e) variables of all individuals in the last generation, (f) the 85% best objective values in the last generation

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Figure 15

A new digital shot-put; (a) prototype, (b) mechanical shell

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Figure 16

The prototype of a unique parallel robotic machine

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