Transactions of the Korean Society of Automotive Engineers (한국자동차공학회논문집)

The Korean Society of Automotive Engineers (한국자동차공학회)
권호 표지

Empirical Bushing Model using Artificial Neural Network

인공신경망을 이용한 실험적 부싱모델링

  • Published : 2003.07.01
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper, a blackbox approach is carried out to model the nonlinear dynamic bushing model. One-axis durability test is performed to describe the mechanical behavior of typical vehicle elastomeric components. The results of the tests are used to develop an empirical bushing model with an artificial neural network. The back propagation algorithm is used to obtain the weighting factor of the neural network. Since the output for a dynamic system depends on the histories of inputs and outputs, Narendra algorithm of 'NARMAX' form is employed to consider these effects. A numerical example is carried out to verify the developed bushing model.

Keywords

References

  1. Transactions of KSAE v.6 no.6 Development of CAE Tools for Vechicle Suspension Design (Ⅰ) - Development of a Bushing Module Y.C.Choi;K.S.Kim;W.S.Yoo;O.J.Kim
  2. CADSI, DADS Revision 8.0 User's Manual
  3. M.D.I., ADAMS Version 8.0 User's Guide
  4. Int. J. of Non-Linear Mechanics v.35 no.2000 A Model for Nonlinear Viscoelastic Coupled Mode Response of an Elastomeric Bushings S.B.Lee;A.Wineman https://doi.org/10.1016/S0020-7462(98)00061-4
  5. SAE 940248 Modeling of Shock Absorber Behavior using Artificial Neural Networks J.W.Fash
  6. KSPE v.14 no.7 Empirical Closed Loop Modeling of a Suspension System Using Neural Network I.Y.Kim;K.T.Chong;T.S.No;D.P.Hong
  7. SAE 2000-01-1625 Accurate Models for Complex Vehicle Components using Empirical Methods A.J.barber
  8. IEEE Transactions on Neural Networks v.1 no.1 Identification and Control of Dynamic System Using Neural Networks K.S.Narendra;K.Pathasarathy https://doi.org/10.1109/72.80202
  9. SAE 970099 Testing and Characterization of Elastomeric Bushings for Large Deflection Behavior E.Y.Kuo