Investigation of the Optimization Numerical of the Design and Operational Conditions of Clutch System by Using the Integration of the Taguchi and Multi-criteria Decision-Making Methods

Iman Q. Saffar (), Kareem Salloomi (), Moneer H. Tolephih (), M. N. Mohammed () and Oday I. Abdullah ()
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Iman Q. Saffar: University of Baghdad
Kareem Salloomi: Al-Khwarizmi College of Engineering, University of Baghdad
Moneer H. Tolephih: Al-Naji University
M. N. Mohammed: Gulf University
Oday I. Abdullah: University of Baghdad

SN Operations Research Forum, 2025, vol. 6, issue 1, 1-20

Abstract: Abstract The clutch system is essential for gently transmitting power from the engine to the gearbox, in addition to other tasks such as absorbing vibration, inertia reducing, and working as a protector against the overload for the motor and the components of power transmission. A new approach is developed for this research paper, to find a dry clutch system’s optimal design and operational parameters to improve performance and thermal behavior. Three design factors were selected to design the numerical tests based on the Taguchi method which are the friction material, the applied pressure function, and the dimensionless radius ratio R. A three-dimensional finite element model was developed, and the numerical cases were designed using the Taguchi matrix of (L9). The maximum surface temperature of the clutch system is considered the criterion for optimal operating conditions. Furthermore, the numerical results were statistically analyzed using Minitab 18, and an ANOVA table was displayed to show the significance and contribution of the new statistical model. In order to validate the results using the Taguchi method, it was applied the multi-criteria decision-making method (weighted product model). The results showed that the most effective parameters on the thermal behavior of the system were the dimensionless radius ratio R with 53.45% and the pressure function with 45.92%.

Keywords: Friction clutch; Taguchi method; Weighted sum model; FE method; Statistical analysis; Optimization (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s43069-025-00423-2

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