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Grey wolf optimizer-based design of ventilated brake disc

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Abstract

Ventilated brake discs used in automobiles are normally subjected to fluctuating load because of their intermittent operations which is principally responsible for their fatigue failure. Premature fatigue failures can be avoided by optimally designing those brake discs. Due to expensive and time-consuming nature of physical experiments, comprehensive design analysis is carried out in this paper in silico to search out the optimal design of ventilated brake disc. Finite element analysis (FEA)-based models are first developed to simulate the fatigue life and axial deflection of ventilated brake discs. While considering five important brake disc design parameters, i.e. inboard plate thickness, outboard plate thickness, vane height, effective offset and center hole radius, 27 FEA simulations are performed based on the central composite design plan. This dataset is then employed for developing two polynomial regression-based metamodels. After critical evaluation of those metamodels with respect to various statistical measures, they are adopted as inexpensive and readily deployable alternatives to the FEA models. Finally, a recently developed metaheuristic algorithm in the form of grey wolf optimizer is applied to optimize the ventilated brake disc design which provides approximately 20% improvement over the baseline model and is 21% better than the multi-criteria decision model-based solutions.

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Abbreviations

F pad :

Braking force

M :

Mass of the vehicle

V o :

Initial velocity

R rotor :

Effective rotor radius

R tire :

Radius of the tire

t stop :

Time to stop

A s :

Surface area of the pad

µ :

Coefficient of friction

p :

Braking pressure

ω :

Rotational speed

T i :

Inboard plate thickness

T o :

Outboard plate thickness

H v :

Vane height

E o :

Effective offset

r :

Center hole radius

x i :

Design variable in optimization process

\(x_{i}^{l}\) :

Lower limit for design variable

\(x_{i}^{u}\) :

Upper limit for design variable

R 2 :

Coefficient of determination or goodness of fit

Adj R 2 :

Adjusted R2

Pred R 2 :

Predicted R2

\(Q_{F1}^{2}\) :

Metamodel quality assessment metric proposed by Shi et al. [1]

\(Q_{F2}^{2}\) :

Metamodel quality assessment metric proposed by Hawkins [2]

\(Q_{F3}^{2}\) :

Metamodel quality assessment metric proposed by Consonni et al. [3]

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Authors and Affiliations

  1. Department of Mechanical Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, India

    Kanak Kalita

  2. Research and Development Department, Elecon Engineering Company Limited, V.V Nagar, Gujarat, India

    Dinesh Shinde

  3. Production Engineering Department, Jadavpur University, Kolkata, India

    Shankar Chakraborty

Authors
  1. Kanak Kalita
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  2. Dinesh Shinde
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  3. Shankar Chakraborty
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Correspondence to Shankar Chakraborty.

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Kalita, K., Shinde, D. & Chakraborty, S. Grey wolf optimizer-based design of ventilated brake disc. J Braz. Soc. Mech. Sci. Eng. 43, 405 (2021). https://doi.org/10.1007/s40430-021-03125-y

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