Fast Vibroacoustic Optimization of Mechanical Structures Using Artificial Neural Networks

Mostafa Ranjbar; Steffen Marburg

doi:doi:10.11648/j.ijmea.20130103.11

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Fast Vibroacoustic Optimization of Mechanical Structures Using Artificial Neural Networks

Mostafa Ranjbar, Steffen Marburg

Published in International Journal of Mechanical Engineering and Applications (Volume 1, Issue 3)

Received: 31 July 2013 Accepted: Published: 20 August 2013

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Abstract

An artificial neural network (ANN) is adjusted to make analytical approximation of objective function for a specific structural acoustic application. It is used as the replacement of the main real objective function during the optimization process. The goal of optimization is to find the best geometry modification of the considered model which is supposed to produce lower values of the radiated sound power levels. The result of this study shows that the function approximation by neural networks can reduce the duration of optimization procedure. Furthermore, the tuning of ANN internal parameter settings is a real challenge to be considered.

Published in	International Journal of Mechanical Engineering and Applications (Volume 1, Issue 3)
DOI	10.11648/j.ijmea.20130103.11
Page(s)	64-68
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Design, Optimization, Vibroacoustic, Artificial Neural Networks, Mechanical Structures, Rectangular Plate

References

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APA Style

Mostafa Ranjbar, Steffen Marburg. (2013). Fast Vibroacoustic Optimization of Mechanical Structures Using Artificial Neural Networks. International Journal of Mechanical Engineering and Applications, 1(3), 64-68. https://doi.org/10.11648/j.ijmea.20130103.11

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ACS Style

Mostafa Ranjbar; Steffen Marburg. Fast Vibroacoustic Optimization of Mechanical Structures Using Artificial Neural Networks. Int. J. Mech. Eng. Appl. 2013, 1(3), 64-68. doi: 10.11648/j.ijmea.20130103.11

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AMA Style

Mostafa Ranjbar, Steffen Marburg. Fast Vibroacoustic Optimization of Mechanical Structures Using Artificial Neural Networks. Int J Mech Eng Appl. 2013;1(3):64-68. doi: 10.11648/j.ijmea.20130103.11

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@article{10.11648/j.ijmea.20130103.11,
  author = {Mostafa Ranjbar and Steffen Marburg},
  title = {Fast Vibroacoustic Optimization of Mechanical Structures Using Artificial Neural Networks},
  journal = {International Journal of Mechanical Engineering and Applications},
  volume = {1},
  number = {3},
  pages = {64-68},
  doi = {10.11648/j.ijmea.20130103.11},
  url = {https://doi.org/10.11648/j.ijmea.20130103.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijmea.20130103.11},
  abstract = {An artificial neural network (ANN) is adjusted to make analytical approximation of objective function for a specific structural acoustic application. It is used as the replacement of the main real objective function during the optimization process. The goal of optimization is to find the best geometry modification of the considered model which is supposed to produce lower values of the radiated sound power levels. The result of this study shows that the function approximation by neural networks can reduce the duration of optimization procedure. Furthermore, the tuning of ANN internal parameter settings is a real challenge to be considered.},
 year = {2013}
}

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TY  - JOUR
T1  - Fast Vibroacoustic Optimization of Mechanical Structures Using Artificial Neural Networks
AU  - Mostafa Ranjbar
AU  - Steffen Marburg
Y1  - 2013/08/20
PY  - 2013
N1  - https://doi.org/10.11648/j.ijmea.20130103.11
DO  - 10.11648/j.ijmea.20130103.11
T2  - International Journal of Mechanical Engineering and Applications
JF  - International Journal of Mechanical Engineering and Applications
JO  - International Journal of Mechanical Engineering and Applications
SP  - 64
EP  - 68
PB  - Science Publishing Group
SN  - 2330-0248
UR  - https://doi.org/10.11648/j.ijmea.20130103.11
AB  - An artificial neural network (ANN) is adjusted to make analytical approximation of objective function for a specific structural acoustic application. It is used as the replacement of the main real objective function during the optimization process. The goal of optimization is to find the best geometry modification of the considered model which is supposed to produce lower values of the radiated sound power levels. The result of this study shows that the function approximation by neural networks can reduce the duration of optimization procedure. Furthermore, the tuning of ANN internal parameter settings is a real challenge to be considered.
VL  - 1
IS  - 3
ER  -

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Author Information

Mostafa Ranjbar

Department of Mechanical Engineering, Faculty of Engineering, Eastern Mediterranean University, Gazimagusa, Turkey
Steffen Marburg

LRT4, Institut für Mechanik, Universit?t der Bundeswehr München, Neubiberg, Germany

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APA Style

Mostafa Ranjbar, Steffen Marburg. (2013). Fast Vibroacoustic Optimization of Mechanical Structures Using Artificial Neural Networks. International Journal of Mechanical Engineering and Applications, 1(3), 64-68. https://doi.org/10.11648/j.ijmea.20130103.11

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ACS Style

Mostafa Ranjbar; Steffen Marburg. Fast Vibroacoustic Optimization of Mechanical Structures Using Artificial Neural Networks. Int. J. Mech. Eng. Appl. 2013, 1(3), 64-68. doi: 10.11648/j.ijmea.20130103.11

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AMA Style

Mostafa Ranjbar, Steffen Marburg. Fast Vibroacoustic Optimization of Mechanical Structures Using Artificial Neural Networks. Int J Mech Eng Appl. 2013;1(3):64-68. doi: 10.11648/j.ijmea.20130103.11

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@article{10.11648/j.ijmea.20130103.11,
  author = {Mostafa Ranjbar and Steffen Marburg},
  title = {Fast Vibroacoustic Optimization of Mechanical Structures Using Artificial Neural Networks},
  journal = {International Journal of Mechanical Engineering and Applications},
  volume = {1},
  number = {3},
  pages = {64-68},
  doi = {10.11648/j.ijmea.20130103.11},
  url = {https://doi.org/10.11648/j.ijmea.20130103.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijmea.20130103.11},
  abstract = {An artificial neural network (ANN) is adjusted to make analytical approximation of objective function for a specific structural acoustic application. It is used as the replacement of the main real objective function during the optimization process. The goal of optimization is to find the best geometry modification of the considered model which is supposed to produce lower values of the radiated sound power levels. The result of this study shows that the function approximation by neural networks can reduce the duration of optimization procedure. Furthermore, the tuning of ANN internal parameter settings is a real challenge to be considered.},
 year = {2013}
}

Copy | Download

TY  - JOUR
T1  - Fast Vibroacoustic Optimization of Mechanical Structures Using Artificial Neural Networks
AU  - Mostafa Ranjbar
AU  - Steffen Marburg
Y1  - 2013/08/20
PY  - 2013
N1  - https://doi.org/10.11648/j.ijmea.20130103.11
DO  - 10.11648/j.ijmea.20130103.11
T2  - International Journal of Mechanical Engineering and Applications
JF  - International Journal of Mechanical Engineering and Applications
JO  - International Journal of Mechanical Engineering and Applications
SP  - 64
EP  - 68
PB  - Science Publishing Group
SN  - 2330-0248
UR  - https://doi.org/10.11648/j.ijmea.20130103.11
AB  - An artificial neural network (ANN) is adjusted to make analytical approximation of objective function for a specific structural acoustic application. It is used as the replacement of the main real objective function during the optimization process. The goal of optimization is to find the best geometry modification of the considered model which is supposed to produce lower values of the radiated sound power levels. The result of this study shows that the function approximation by neural networks can reduce the duration of optimization procedure. Furthermore, the tuning of ANN internal parameter settings is a real challenge to be considered.
VL  - 1
IS  - 3
ER  -

Copy | Download