Skip to main content

Advertisement

SpringerLink
Log in

A neural–genetic (NN–GA) approach for optimising mechanisms having joints with clearance

  • Published:
Multibody System Dynamics Aims and scope Submit manuscript

    We’re sorry, something doesn't seem to be working properly.

    Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Abstract

In this paper, a dimensional synthesis method for a four-bar (4R) path generator mechanism having revolute joints with clearance is presented. Joint clearances are considered as virtual massless links. The proposed method uses a neural network (NN) to define the characteristics of joints with clearance with respect to the position of the input link, and a genetic algorithm (GA) to implement the optimization of link parameters using an appropriate objective function based on path and transmission angle errors. Training and testing data sets for network weights are obtained from mechanism simulation, and Grashof’s rule is used during the optimization process as constraint. The results show that the proposed method is very efficient for the purpose of modeling the joint variables and also adjusting the link dimensions to optimize planar mechanisms with clearances.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Abbreviations

BP:

Back propagation

c :

Value with clearance

d :

Desired value

e :

Error

GA:

Genetic algorithm

G i :

Gravity center of the ith mechanism link

g k :

Constraint

L i :

The ith link length

MLNN:

Multi-layered neural network

NN:

Neural network

P x :

x-coordinate value for the path of point P

P y :

y-coordinate value for the path of point P

r B :

Bearing radius

r j :

The jth actual clearance value

r J :

Journal radius

s :

The number of considered points in objective function

W :

Weighting factor in objective function

w :

Network weight

X :

Design variable vector

x r :

Independent design variables

x min  r :

Lower bounds of design variables

x max  r :

Upper bounds of design variables

γ j :

Angular direction of the jth joint clearance

μ :

Transmission angle

β :

Structural angle

References

  1. Sandor, G.N., Erdman, A.G.: Advanced Mechanism Design Analysis and Synthesis. Prentice-Hall, Englewood Cliffs (1984)

    Google Scholar 

  2. Laribi, M.A., Mlika, A., Romdhane, L., Zeghloul, S.: A combined genetic algorithm–fuzzy logic method (GA–FL) in mechanisms synthesis. Mech. Mach. Theory 39, 717–735 (2004)

    Article  MATH  Google Scholar 

  3. Sancibrian, R., Garcia, P., Viadero, F., Fernandez, A.: A general procedure based on exact gradient determination in dimensional synthesis of planar mechanisms. Mech. Mach. Theory 41, 212–229 (2006)

    Article  MATH  MathSciNet  Google Scholar 

  4. Zhou, H., Cheung, E.H.M.: Optimal synthesis of crank-rocker linkages for path generation using the orientation structural error of the fixed link. Mech. Mach. Theory 36, 973–982 (2001)

    Article  MATH  Google Scholar 

  5. Kunjur, A., Krishnamurty, S.: Genetic algorithms in mechanism synthesis. J. Appl. Mech. Robot. 4, 18–24 (1997)

    Google Scholar 

  6. Prebil, I., Krasna, S., Ciglaric, I.: Synthesis of four-bar mechanism in a hydraulic support using a global optimization algorithm. Struct. Multidiscipl. Optim. 24, 246–251 (2002)

    Article  Google Scholar 

  7. Smaili, A., Diab, N.: Optimum synthesis of hybrid-task mechanism using ant gradient search method. Mech. Mach. Theory 42, 115–130 (2007)

    Article  MATH  MathSciNet  Google Scholar 

  8. Vasiliu, A., Yannou, B.: Dimensional synthesis of planar mechanisms using neural networks: application to path generator linkages. Mech. Mach. Theory 36, 299–310 (2001)

    Article  MATH  Google Scholar 

  9. Yıldırım, Ş., Erkaya, S., Su, Ş., Uzmay, İ.: Design of neural networks model for transmission angle of a modified mechanism. KSME Int. J. 19, 1875–1884 (2005)

    Google Scholar 

  10. Shi, Z., Yang, X., Yang, W., Cheng, Q.: Robust synthesis of path generating linkages. Mech. Mach. Theory 40, 45–54 (2005)

    Article  MATH  MathSciNet  Google Scholar 

  11. Cabrera, J.A., Simon, A., Prado, M.: Optimal synthesis of mechanisms with genetic algorithms. Mech. Mach. Theory 37, 1165–1177 (2002)

    Article  MATH  Google Scholar 

  12. Ting, K.W., Zhu, J., Watkins, D.: The effects of joint clearance on position and orientation deviation of linkages and manipulators. Mech. Mach. Theory 35, 391–401 (2000)

    Article  MATH  Google Scholar 

  13. Tsai, M.J., Lai, T.H.: Kinematic sensitivity analysis of linkage with joint clearance based on transmission quality. Mech. Mach. Theory 39, 1189–1206 (2004)

    Article  MATH  Google Scholar 

  14. Flores, P., Ambrosio, J., Claro, J.P.: Dynamic analysis for planar multibody mechanical systems with lubricated joints. Multibody Syst. Dyn. 12, 47–74 (2004)

    Article  MATH  Google Scholar 

  15. Flores, P., Ambrosio, J., Claro, J.C.P., Lankarani, H.M., Koshy, C.S.: A study on dynamics of mechanical systems including joints with clearance and lubrication. Mech. Mach. Theory 41, 247–261 (2006)

    Article  MATH  Google Scholar 

  16. Choi, J.H., Lee, S.J., Choi, D.H.: Tolerance optimization for mechanisms with lubricated joints. Multibody Syst. Dyn. 2, 145–168 (1998)

    Article  MATH  Google Scholar 

  17. Ravn, P.: A continuous analysis method for planar multibody systems with joint clearance. Multibody Syst. Dyn. 2, 1–24 (1998)

    Article  MATH  Google Scholar 

  18. Chiou, S.T., Bai, G.J., Chang, W.K.: Optimum balancing designs of the drag-link drive of mechanical presses for precision cutting. Int. J. Mach. Tools. Manuf. 38, 131–141 (1998)

    Article  Google Scholar 

  19. Martinez-Alfaro, H., Valdez, H., Ortega, J.: Linkage synthesis of a four-bar mechanism for n-precision points using simulated annealing, DETC98/ MECH-5942. In: Proc. of the 1998 ASME Design Engineering Technical Conferences, Atlanta, Georgia, USA (1998)

Download references

Author information

Authors and Affiliations

  1. Engineering Faculty, Department of Mechanical Engineering, Erciyes University, Kayseri, Turkey

    Selçuk Erkaya & İbrahim Uzmay

Authors
  1. Selçuk Erkaya
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. İbrahim Uzmay
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to İbrahim Uzmay.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Erkaya, S., Uzmay, İ. A neural–genetic (NN–GA) approach for optimising mechanisms having joints with clearance. Multibody Syst Dyn 20, 69–83 (2008). https://doi.org/10.1007/s11044-008-9106-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11044-008-9106-6

Keywords

Search

Navigation