In wet clutches, load-independent drag losses occur in the disengaged state and under differential speed due to fluid shearing. The drag torque of a wet clutch can be determined accurately and reliably by means of costly and time-consuming measurements. As an alternative, the drag losses can already be precisely calculated in the early development phase using computing-intensive CFD models. In contrast, simple analytical calculation models allow a rough but non-time-consuming estimation. Therefore, the aim of this study was to develop a methodology that can be used to build a data-driven model for the prediction of the drag losses of wet clutches with low computational effort and, at the same time, sufficient accuracy under consideration of a high number of influencing parameters. For building the model, we use supervised machine learning algorithms. The methodology covers all relevant steps, from data generation to the validated prediction model as well as its usage. The methodology comprises six main steps. In Step 1, the data is generated on a suitable test rig. In Step 2, characteristic values of each measurement are evaluated to quantify the drag loss behavior. The characteristic values serve as target values to train the model. In Step 3, the structure and quality of the dataset are analyzed and, subsequently, the model input parameters are defined. In Step 4, the relationships between the investigated influencing parameters (model input) and the characteristic values (model output) are determined. Symbolic regression and Gaussian process regression have both been proven to be suitable for this task. Lastly, the model is used in Step 5 to predict the characteristic values. Based on the predictions, the drag torque can be predicted as a function of differential speed in Step 6, using an approximation function. The model allows a user-oriented prediction of the drag torque even for a high number of parameters with low computational effort and sufficient accuracy at the same time.

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For wet disk clutches, the energy input is strongly influenced by its friction behavior. However, the friction behavior cannot be simulated and therefore is mostly derived from experimental data for specific clutch systems. This paper presents a new approach for the identification and validation of linear friction models using analysis of variance (ANOVA) and stepwise regression. Therefore, we use experimental data of three different friction systems with paper- and carbon-based friction lining. The designed experiments support an efficient parameter-based analysis of the friction behavior. The obtained models can be used as an input for thermal simulations, for example, but can also support a better understanding of the main influencing factors and are applicable to various friction systems. For validation, the obtained models are applied to measured data. A good correspondence between the simulated and measured friction behavior can be shown for speeds in the investigated operating range. The presented procedure can be easily adapted, for different factors and operation modes, as well as other applications.