Life and Risk Assessment of Semiactive Suspension System Using Ride Comfort Advisory System (RCAS)

  • R. N. YerrawarEmail author
  • M. A. Joshi
  • R. R. Arakerimath
Conference paper


In this paper, the design of experiment approach is used for analysis and optimization of ride comfort of the vehicle. Also, optimized suspension parameters combination was presented through Taguchi DOE method. From Taguchi L16 array and SN ratio analysis, it is observed that the Cylinder Material with Al and CS for damper cylinder is a key parameter for performance measure of Semi active suspension system. From regression analysis, linear mathematical model is developed. The optimized Ride Comfort observed from the DOE table is 0.99 m/s2 which is in the range of Fairly Uncomfortable to Uncomfortable as per IS 2631. The advisory system, known as Ride Comfort Advisory System (RCAS) is developed. The optimized model of Magnetorheological (MR) damper will have widely helpful to Indian Auto Industry. From the measured acceleration and RCAS acceleration results, it is observed that there is error between ±8 to ±10% and it is under limit as per comfort level suggested by IS 2631. The error arise due to the linear model equations and actual readings.


Semiactive suspension Ride comfort Magnetorheological damper 


  1. 1.
    Lam, H.F., Lai, C.Y., Liao W.H.: Automobile suspension systems with MR fluid dampers, smart materials and structures laboratory, Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, pp. 1–18 (2002)Google Scholar
  2. 2.
    ISO 2631-1:1997: Mechanical vibration and shock, evaluation of human exposure to whole-body vibration, Part 1: General requirements, 2,1–31 (1997)Google Scholar
  3. 3.
    Mathews P.G.: Design of experiments with MINITAB, Classroom Exercises and Labs (2007)Google Scholar
  4. 4.
    Roy R.: Design of Experiments Using Taguchi Approach. John Wiley Inc (2001)Google Scholar
  5. 5.
    Devdutt and Aggarwal M. L, Simultaneous optimization of semi active quarter car suspension parameters using taguchi method and grey relational analysis. Int. J. Recent Adv. Mech. Eng. 4(1) (2015)Google Scholar
  6. 6.
    Argade, P.V., Arakerimath, R.R.: Parametric investigations on CO2 laser cutting of AISI 409 to optimize process parameters by Taguchi method. Int. J. Eng. Trends Technol. (IJETT) 37(6), 1–6 (2016)Google Scholar
  7. 7.
    Nemani, R., Arakerimath, R.R.: Taguchi based design optimization of side impact beam for energy absorption. Int. J. Adv. Res. Eng. Technol. 3, 9 (2015)Google Scholar
  8. 8.
    Liao, H.T., Enke, D., Wiebe, H.: An expert advisory system for the ISO 9001 quality system. Expert Syst. Appl. 27, 313–322 (2004)CrossRefGoogle Scholar
  9. 9.
    Mansyur, R., Atiq, R., Rahmat, O.K., Ismail, A., Kabit, M.R.: Knowledge based expert advisory system for transport demand management. In: Proceeding of the International Conference on Advanced Science, Engineering and Information Technology, pp. 652–657 (2011)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • R. N. Yerrawar
    • 1
    Email author
  • M. A. Joshi
    • 1
  • R. R. Arakerimath
    • 2
  1. 1.MES College of EngineeringPuneIndia
  2. 2.G. H. Raisoni COE and MWagholi, PuneIndia

Personalised recommendations