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Analysis of the Experimental Torque Characteristic of a Magnetorheological Clutch Using Neural Networks

[+] Author Affiliations
Francesco Bucchi, Paola Forte, Francesco Frendo

University of Pisa, Pisa, Italy

Paper No. SMASIS2013-3114, pp. V001T03A023; 9 pages
  • ASME 2013 Conference on Smart Materials, Adaptive Structures and Intelligent Systems
  • Volume 1: Development and Characterization of Multifunctional Materials; Modeling, Simulation and Control of Adaptive Systems; Integrated System Design and Implementation
  • Snowbird, Utah, USA, September 16–18, 2013
  • Conference Sponsors: Aerospace Division
  • ISBN: 978-0-7918-5603-1
  • Copyright © 2013 by ASME


In this paper the dependence of the torque characteristic of a magnetorheological clutch on several working parameters is analysed by means of a feedforward neural network. The clutch was envisaged to have the possibility to disengage the vacuum pump in diesel engine vehicles, in order to increase the overall vehicle efficiency.

A large set of test was carried out following different protocols in order to obtain a detailed characterization of the clutch. Results showed how, due to the characteristics of MR fluids and to the complex mechanism of torque transmission, the torque characteristics (both the yield torque and the torque-slip behaviour) is function of the relative speed between the primary and secondary clutch groups and also function of the dissipated energy during the clutch slip and of the rest time between consecutive slippages. The data acquired during all tests were used to train a neural network with five input elements and one output element.

The developed neural network was proved to satisfactorily reproduce the actual clutch properties and can be used in the future in an engine-vehicle simulator in order to model the torque characteristic of the clutch subjected to different operating cycles.

Copyright © 2013 by ASME



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