In the automotive industry, the perception of the stick-slip effect, which can occur between the brake disc and the brake pads during slow acceleration, is important in terms of noise pleasantness. Since the subjective quantification of unpleasant brake sound is expensive and time-consuming, it would make sense to replace the subjective evaluation with a machine learning algorithm. In order to replace subjective evaluation with a machine learning algorithm, several methods of supervised and unsupervised learning were tested on a large number of features obtained from experimental brake tests. Based on the results, a small number of important features was selected the algorithms were trained only with the selected features. The algorithms were compared based on the results. The self-organizing map and the k-means algorithm proved to be the most appropriate algorithms. The results obtained using four selected features and six classes proved to be more reliable and reproducible than the obtained subjective evaluations.
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