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 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), 2013. Published by Science Publishing Group

Keywords

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

References

[1]	St. Marburg, "Developments in Structural–Acoustic Optimization for Passive Noise Control," Archives of Computational Methods in Engineering. State of the art reviews, vol. 9, 2002, pp. 291–370.
[2]	M. A. Arslan, and P. Hajela, "Use of Counterpropagation Neural Nnetworks to Enhance the Concurrent Subspace Optimization Strategy," Engineering Optimization, vol. 33, 2001, pp. 327–349.
[3]	K. H. Baek, and S. J. Elliott, "Natural Algorithms for Choosing Source Locations in Active Control Systems," Journal of Sound and Vibration, vol. 186 (2), 1995, pp. 245–267.
[4]	K. Nagaya, and L. Li, "Control of Sound Noise Radiated from a Plate using Dynamic Absorbers under the Optimization by Neural Network,"Journal of Sound and Vibration, vol. 208, 1997, pp. 289–298.
[5]	Q. Shi, I. Hagiwara, A. Azetsu, and T. Ichkawa, "Holographic Neural Network Approximations for Acoustic Optimization," JSAE Review, vol. 19, 1998, pp. 361–363.
[6]	S. Kirkpatrick, C. D. Jr. Gellat, and M. P. Vecchi, " Optimization by Simulated Annealing," Science, vol. 220, 1983, pp. 671–680.
[7]	W. Goffe, G. D. Ferrier, and J. Rogers, "Global Optimization of Statistical Functions with Simulated Annealing," Journal of Econometrics, vol. 60 (1-2), 1994, pp. 65–99.
[8]	W. Goffe, "SIMANN: A Global Optimization Algorithm Using Simulated Annealing," Studies in Non-linear Dynamics and Econometrics, vol. 1 (3), 1996, pp. 169–176.
[9]	A. Corana, M. Marchesi, C. Martini, and S. Ridella, "Minimizing Multimodal Functions of Continuous Variables with the Simulated Annealing Algorithm," ACM (Association for Computing Machinery) Transactions on Mathematical Software, vol. 13 (3), 1987, pp. 262–280.
[10]	J. A. Nelder, and R. Mead, " A Simplex Method for Function Minimization,"The Computer Journal, vol. 7 (4), 1965, pp. 308–313.
[11]	H. H. Rosenbrock, " An Automatic Method for Finding the Greatest or Least Value of a Function," The Computer Journal, vol. 3 (3), 1960, pp. 175–184.
[12]	E. W. Constans, G. H. Koopmann, and A. D. Belegundu, "The Use of Modal Tailoring to Minimize the Radiated Sound Power of Vibrating Shells: Theory and Experiment," Journal of Sound and Vibration, vol. 217 (2), 1998, pp. 335–350.
[13]	P. Y. Shim, and S. Manoochehri, "A Hybrid Deterministic/Stochastic Optimization Approach for the Shape Configuration Design of Structures," Structural and Multidisciplinary Optimization, vol. 17 (2-3), 1999, pp. 113–129.
[14]	M. Ranjbar, St. Marburg, and H.-J. Hardtke, "Development of a Hybrid Neural Networks Algorithm for Structural-Acoustics Optimization Applications", In Proceedings of the First International Conference of Acoustics and Vibration, 21-22 December 2011, Tehran, Iran.
[15]	M. Ranjbar, A Comparative Study on Optimization in Structural Acoustics, PhD Thesis, Technische Universität Dresden, Germany, 2011.
[16]	G. H. Koopmann, and J. b. Fahnline, Designing Quiet Structures: A Sound Power Minimization Approach, Academic Press, San Diego, London, 1997.
[17]	F. G. Kollmann, Maschinenakustik--Grundlagen, Meßtechnik, Berechnung, Beeinflussung, in German, 2nd revised edition, Springer-Verlag, Berlin, Heidelberg, 2000.
[18]	S. Marburg, "Efficient Optimization of a Noise Transfer Function by Modification of a Shell Structure Geometry. Part I: Theory," Structural and Multidisciplinary Optimization, Vol. 24, 2002. pp. 51–59.
[19]	D. Fritze, St. Marburg, and H.-J. Hardtke, "Estimation of Radiated Sound Power: A Case Study on Common Approximation Methods," Acta Acustica united with Acustica, vol. 95, 2009, pp. 833–842.
[20]	Ranjbar, H.-J. Hardtke, D. Fritze, and St. Marburg, "Finding the Best Design within Limited Time: A Comparative Case Study on Methods for Optimization in Structural Acoustics," Journal of Computational Acoustics, vol. 18 (2), 2010, pp. 149-164.
[21]	M. Ranjbar, St. Marburg, and H.-J. Hardtke, " Structural-Acoustic Optimization of a Rectangular Plate: A Tabu Search Approach," Journal of Finite Elements in Analysis and Design, vol. 50, 2012, pp. 142-146.
[22]	M. Ranjbar, and St. Marburg, " Vibroacoustic Optimization of Mechanical Structures: A Controlled Random Search Approach," Advanced Materials Research, vol. 623, 2013, pp. 158-161.
[23]	St. Marburg, "Efficient Optimization of a Noise Transfer Function by Modification of a Shell Structure Geometry. Part I: Theory," Structural and Multidisciplinary Optimization, vol. 24, 2002, pp. 51–59.
[24]	ANSYS® Academic Research, Release 11.0, Help System, ANSYS, Inc.
[25]	M. Papadrakakis, N. D. Lagaros, and Y. Tsompanakis, "Optimization of Large-Scale 3-D Trusses Using Evolution Strategies and Neural Networks," Special Issue of International Journal of Space Structures, vol. 14, 1999, pp. 211–223.

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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