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Clutches are mechanisms used for coupling between shafts in order to transmit torque from one to the other. This coupling is made mechanically by friction between the parts with a high friction intermediate material. In this process, the slippage between the parts becomes a source of heat that makes the system temperature to raise up to high values. Under high temperature, the capacity of torque transmission of the clutch can be reduced by the variation of the effective contact diameter, once the contact region of friction change as the temperature is rising. This is caused by the thermal-displacement effect induced by the friction. The torque capacity also can be affected by the friction coefficient that varies with the temperature. Therefore, in order to design an optimized system, it is necessary an analysis of the parts and materials under the influence of temperature changing.

The rolling bearing is a fundamental component of rotating machines and its failure may lead to a catastrophic damage of the system. The incipient and correct identification of faults contribute to an early predictive maintenance plan, which avoids additional costs and sudden breakdowns. The resonant demodulation technique, envelope analysis, is a well-established method widely used to identify failures in rolling bearings. However, this method requires the identification of the frequency region that contains enough information about the faults. Thus, the spectral kurtosis gives the impulsiveness measure of a vibration signal and it is used to identify the frequency region of failure. This paper presents the use of the bat algorithm as an optimization methodology to identify the resonant demodulation parameters using spectral kurtosis as the objective function.

Gear design techniques usually results on heavy drives that are contradictory to the tendency of mass reduction on the modern road vehicles applications. This paper proposes the utilization of a genetic algorithm to design and optimize geometric parameters of a transmission stage comprised of two steel gears. Although genetic algorithm is not an optimization technique on a straight sense, its use is increasing in several applications where the design space is multidimensional. In this study, the first fitness function proposed is a weighted average of critical performance factors on gears design such as total drive weight, tooth contact stress, bending stress and total space occupied by the assembly and the second fitness proposed considers the center distance influence and the tooth contact stress and bending stress. The weights of the average are modified in several simulations.