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Research Papers: Design of Mechanisms and Robotic Systems

An Optimization Approach Toward a Robust Design of Six Degrees of Freedom Haptic Devices

[+] Author and Article Information
Aftab Ahmad

Department of Machine Design,
KTH Royal Institute of Technology,
Stockholm 100 44, Sweden
e-mail: aftaba@kth.se

Kjell Andersson

Department of Machine Design,
KTH Royal Institute of Technology,
Stockholm 100 44, Sweden
e-mail: kan@kth.se

Ulf Sellgren

Department of Machine Design,
KTH Royal Institute of Technology,
Stockholm 100 44, Sweden
e-mail: ulfs@md.kth.se

Contributed by the Mechanisms and Robotics Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received January 9, 2014; final manuscript received December 30, 2014; published online February 6, 2015. Assoc. Editor: Xiaoping Du.

J. Mech. Des 137(4), 042301 (Apr 01, 2015) (14 pages) Paper No: MD-14-1012; doi: 10.1115/1.4029514 History: Received January 09, 2014; Revised December 30, 2014; Online February 06, 2015

This work presents an optimization approach for the robust design of six degrees of freedom (DOF) haptic devices. Our objective is to find the optimal values for a set of design parameters that maximize the kinematic, dynamic, and kinetostatic performances of a 6-DOF haptic device while minimizing its sensitivity to variations in manufacturing tolerances. Because performance indices differ in magnitude, the formulation of an objective function for multicriteria performance requirements is complex. A new approach based on Monte Carlo simulation (MCS) was used to find the extreme values (minimum and maximum) of the performance indices to enable normalization of these indices. The optimization approach presented here is formulated as a methodology in which a hybrid design-optimization approach, combining genetic algorithm (GA) and MCS, is first used. This new approach can find the numerical values of the design parameters that are both optimal and robust (i.e., less sensitive to variation and thus to uncertainties in the design parameters). In the following step, with design optimization, a set of optimum tolerances is determined that minimizes manufacturing cost and also satisfies the allowed variations in the performance indices. The presented approach can thus enable the designer to evaluate trade-offs between allowed performance variations and tolerances cost.

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Fig. 2

Optimization approach toward a robust design

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Fig. 3

Kinematic structure of 6-DOF TAU haptic device

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Fig. 4

PDF of performance indices using RDO: (a) PDF of VIn, (b) PDF of GIIn, (c) PDF of GTRIn, (d) PDF of GKIn, (e) PDF of GMIn, and (f) PDF of GNFIn

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Fig. 5

Manufacturing cost to allowed variation in performance indices

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Fig. 6

Deterministic design optimization

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Fig. 7

PDF of performance indices using DDO: (a) PDF of VIn, (b) PDF of GIIn, (c) PDF of GTRIn, (d) PDF of GKIn, (e) PDF of GMIn, and (f) PDF of GNFIn

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Fig. 8

PDF of performance indices using RDO and DDO: (a) PDF of VIn, (b) PDF of GIIn, (c) PDF of GTRIn, (d) PDF of GKIn, (e) PDF of GMIn, and (f) PDF of GNFIn

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Fig. 9

Comparison of sensitives of standard deviation (a) and confidence bound (b) of performance indices using RDO, optimum manufacturing tolerances, and DDO

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