Username   Password       Forgot your password?  Forgot your username? 

 

Fitting Methods based on Custom Neural Network for Relaxation Modulus of Viscoelastic Materials

Volume 15, Number 1, January 2019, pp. 107-115
DOI: 10.23940/ijpe.19.01.p11.107115

Yun Hea,b, Haibin Lia, and Juan Dua

aCollege of Science, Inner Mongolia University of Technology, Hohhot, 010051, China
bCollege of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot, 010018, China

(Submitted on October 6, 2018; Revised on November 8, 2018; Accepted on December 16, 2018)

Abstract:

The viscoelastic material constitutive relationship is relatively complex in the engineering practice. People often use the Prony series method to fit experimental data. A lower number of terms leads to lower accuracy, but a higher number of terms leads to difficulty of fitting. Thus, a custom neural network model was put forward to replace the traditional algorithm in the Prony series fitting process. Based on each specific form of the Prony series constructed corresponding activation function of the hidden layer in the neural network, the number of neurons in the neural network corresponded to the number of the Prony series. A numerical example showed that the custom neural network can achieve good fitting results. It is convenient and appropriate to select the number of terms and also shows rapid convergence and high accuracy.

 

References: 21

      1. R. D. Bradshaw and L. C. Brinson, “A Sign Control Method for Fitting and Interconverting Material Functions for Linearly Viscoelastic Solids,” Mechanics of Three-Dependent Materials, Vol. 1, pp. 85-108, 1997
      2. B. X. Yin, “Curve Fitting Calculate Viscoelastic Material Constant,” Journal of Southwest Institute of Technology, Vol. 14, No. 4, pp. 28-32, 1999
      3. Z. Li, “Viscoelastic Fractional Derivative Model and its Application in Solid Engine,” Tsinghua University, Beijing, 2000
      4. A. H. Jimenez, B. V. Jara, and J. H. Santiago, “Relaxation Modulus in the Fitting of Polycarbonate and Poly(Vinyl Chloride) Viscoelastic Polymers by a Fractional Maxwell Model,” Colloid and Polymer Science, Vol. 280, No. 5, pp. 485-489, 2002
      5. J. W. Huang and B. X. Yin, “The Fitting Method of Viscoelastic Material Constant,” Journal of Huazhong University of Science and Technology, Vol. 22, No. 4, pp. 88-90, 2005
      6. J. A. Duan, C. L. Yang, and C. J. Shuai, “Fitting Methods for Relaxation Modulus of Viscoelastic Materials,” Journal of Central South University of Technology, Vol. 14, No. 2, pp. 248-250, 2007
      7. X. L. Zhan, X. N. Zhang, and D. Y. Wang, “Research and Application of Modified Asphalt Nonlinear Viscoelastic Constitutive Relation,” Journal of Engineering Mechanics, Vol. 26, No. 4, pp. 187-191, 2009
      8. Y. B. Wei, Y. K. Shi, and P. Liu, “Viscoelastic Material Shear Modulus Relaxation Function Fitting Study,” Journal of Binggong, Vol. 31, No. 1, pp. 1409-1412, 2010
      9. H. Yin, D. M. Wang, and C. Zhao, “Asphalt Mixture Fractional Derivative Viscoelastic Constitutive Relation Research,” Forest Engineering, Vol. 26, No. 2, pp. 77-82, 2010
      10. H. Shekhar and A. D. Sahasrabudhe, “Viscoelastic Modelling of Solid Rocket Propellants,” Defence Science Journal, Vol. 60, pp. 423-527, 2010
      11. H. Haario, R. V. Hertzen, and A. T. Karttunen, “Identification of the Viscoelastic Parameters of a Polymer Model by the Aid of a MCMC Method,” Mechanics Research Communications, Vol. 61, pp. 1-6, 2014
      12. M. Gasperlin, L. Tusar, and M. Tusar, “Lipophilic Semisolid Emulsion Systems: Viscoelastic Behaviour and Prediction of Physical Stability by Neural Network Modelling,” International Journal of Pharmaceutics, Vol. 168, No. 2, pp. 243-254, 1998
      13. J. G. Zeng and Y. Q. Shu, “Material Nonlinear Viscoelastic Constitutive Relation of Neural Network Simulation,” Journal of Solid Mechanics, Vol. 1, No. 25, pp. 71-74, 2004
      14. M. S. Al-Haik, M. Y. Hussaini, and H. Garmestani, “Prediction of Nonlinear Viscoelastic Behavior of Polymeric Composites using an Artificial Neural Network,” International Journal of Plasticity, Vol. 22, No. 7, pp. 1367-1392, 2006
      15. M. H. Saeidirad, A. Rohani, and S. Zarifneshat, “Predictions of Viscoelastic Behavior of Pomegranate using Artificial Neural Network and Maxwell Model,” Computers and Electronics in Agriculture, Vol. 98, pp. 1-7, 2013
      16. J. Zheng, B. Han, and C. S. Zhou, “Research of Composite Propellant Viscoelastic Constitutive Parameters based on Genetic Algorithm,” Journal of Ballistic, Vol. 26, No. 1, pp. 22-25, 2014
      17. C. J. Shuai, J. A. Duan, and J. Wang, “Generalized Maxwell Model of Viscoelastic Material,” Journal of Mechanics, Vol. 38, No. 4, pp. 565-569, 2006
      18. Y. B. Gao, “Viscoelastic Analysis of Solid Rocket Propellant based on the BP Neural Network,” Inner Mongolia University of Technology, Hohhot, 2014
      19. T. Q. Yang and W. B. Luo, “Viscoelastic Theory and Application,” Science Press, Beijing, 2004
      20. C. H. Dong, “Matlab Neural Network and Application,” National Defence Industry Press, Beijing, 2007
      21. D. F. Zhang, “Matlab Neural Network Application Design,” Mechanical Industry Press, Beijing, 2009

           

          Please note : You will need Adobe Acrobat viewer to view the full articles.Get Free Adobe Reader

           
          This site uses encryption for transmitting your passwords. ratmilwebsolutions.com