Inverse Estimation of Viscoelastic Material Properties Based on Particle Swarm Optimization and Neural Network

Xing Kai Cheng; Ke Chen; Jiang Li Lin

doi:10.4028/www.scientific.net/AMR.488-489.124

Paper Titles

Optical Properties of NiO Thin Films: A Potential Material for Optoelectronic Devices
p.103

Physical Properties of PP/Recycled PET Blends Prepared by Pulverization Technique
p.109

Transport Properties of Chitosan/Peo Blend Based Proton Conducting Polymer Electrolyte
p.114

Martensitic Transformation and Response Characteristics in Bias-SMA Spring Actuators with Thermal Cycling
p.118

Inverse Estimation of Viscoelastic Material Properties Based on Particle Swarm Optimization and Neural Network
p.124

Ab Initio Electronic Structure Studies of CuO Multiferroics
p.129

Laboratory Study and Evaluation on a Novel Clearfrac Fluid System
p.133

Effects of End-Groups on Properties of Hyperbranched Polyether
p.137

Curing Behavior Characterization of Phenolic Resin Derived from Starch
p.142

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 488-489Inverse Estimation of Viscoelastic Material...

Inverse Estimation of Viscoelastic Material Properties Based on Particle Swarm Optimization and Neural Network

Abstract:

The accuracy of inverse estimation for viscoelastic material properties is usually affected by two main factors: mechanical model and the initial value of iterations. In our research, a new method based on artificial neural network and modified particle swarm optimization (PSO) is developed to estimate viscoelastic material properties. The artificial neural network is established to model the behavior of viscoelastic materials which has solved the model-dependent problem; and chaos algorithm is also added to PSO algorithm to improve its local escaping ability which helps us solve the initial-value problem. Feasibility of this method is demonstrated by both data from theoretical formula and FEM simulation experiment.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 488-489)

Pages:

124-128

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.488-489.124

Citation:

Cite this paper

Online since:

March 2012

Authors:

Xing Kai Cheng, Ke Chen, Jiang Li Lin

Keywords:

Chaos Algorithm, Neural Network (NN), Particle Swarm Optimization Algorithm (PSO), Viscoelasticity

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] B.S. Garra, E.I. Cespedes, J. Ophir, R.S. Spratt, R.A. Zuurbier, C.M. Magnant, and M.F. Pennanen: Radiology. Vol. 202 (1997), p.79–86.

DOI: 10.1148/radiology.202.1.8988195

Google Scholar

[2] Y. C. Fung: Biomechanics: Mechanical Properties of Living Tissues. Springer Publishers (1993).

Google Scholar

[3] S. Chen, M. Fatemi, J .F. Greenleaf: J. Acoust. Soc. Am. Vol. 115 (2004), p.2781–2785.

Google Scholar

[4] Yi Zheng, Shigao Chen, Wei Tan, et al: IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control. Vol. 54 (2007), pp.290-300.

Google Scholar

[5] Shigao Chen, J.F. Greenleaf, Yi Zheng, et al: IEEE International Ultrasonics Symposium Proceedings, (2008), pp.654-657.

Google Scholar

[6] Yi Zheng, Aiping Yao, Shigao Chen, et al: IEEE International Ultrasonics Symposium Proceedings. (2008), pp.217-220.

Google Scholar

[7] Allan F. Bower: Applied Mechanics of Solids. CRC Publishers (2009).

Google Scholar

[8] J. Kennedy, R.C. Eberhart: Proceedings of the IEEE Int Conf on Neural Networks. Perth, Australia: IEEE Press, (1995), p.1942-(1948).

Google Scholar

[9] J.C. Brigham, W. Aquinoa, F.G. Mitri, et al: Journal of Applied Physics. Vol. 101 (2007), pp.1-14.

Google Scholar

[10] Chuanbao Cai, Wencheng Tang: Journal of Applied Sciences. Vol. 26 (2008), pp.295-300.

Google Scholar

[11] S. Girnyk, A. Barannik, E. Barannik, V. Tovstiak, A. Marusenko, and V. Volokhov: Ultrasound in Med. & Biol. Vol. 32 (2006), pp.211-219.

DOI: 10.1016/j.ultrasmedbio.2005.11.015

Google Scholar

[12] M. Fatemi, J.F. Greenleaf: Science. Vol. 280 (1998), pp.82-85.

Google Scholar