SummaryThe purpose of this paper is to investigate experimental and numerical dynamic responses

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The aim of this work is to characterize the rattle noise of automotive gearboxes, resulting from impacts between toothed wheels of unselected gear ratios. These stereo-mechanical impacts are modeled by a coefficient of restitution which describes damping associated with the elastic deformation during the impact and the squeezing of the lubricant film. The dynamic response of loose gear firstly depends on the design parameters and the engine operating conditions. The unknown parameters are the drag torque and the coefficient of restitution. They are identified experimentally through implementation of optical encoders in an actual automotive gearbox and the operation of a specific test bench which replicates the automotive power train. Models of the different drag torque sources are validated from analysis of the free damped response of the drivelines. The coefficient of restitution and its probability density function are measured from experiments under stationary operating conditions. A non linear model is built. The dynamic response of loose gear depends on the dimensionless backlash, the coefficient of restitution and a dimensionless parameter proposed to describe the rattle excitation level. Experiments under controlled excitation are performed to validate the assumptions, to confirm the ability of the parameter proposed to describe the rattle noise threshold, and to characterize the dynamic response. The non linear model predictions are fitted with the drag torque and coefficient of restitution previously identified. They are compared with measurements to demonstrate the ability of the model to predict gear rattle for any loose gear, any gearbox and any operating condition.

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This paper presents a methodology for the multiobjective optimization of both gear macro and microgeometry parameters in order to minimize the Static Transmission Error (STE) and the mesh stiffness fluctuations generated by the meshing process. The optimization is performed using a genetic algorithm which allows testing of a high number of gears. As the repeti