Abstract
Due to the electrification of vehicles, the focus of vehicle noise is shifting to the tires. Since tire noise is dependent of the tread pattern, methods to characterize tread patterns are required. This research investigates the effects of different processes of tire digitization on the identification of tread pattern features. An open source digitization pipeline is built to extract a tessellation surface model. This model is compared to one, generated with a commercial photogrammetric system. The comparison is based on three acoustically relevant tire features. Fully automated algorithms are introduced to extract these features from any 3D tire tessellation model. The research finds that although the resolution of the mesh surface of the proposed model is lower, feature recognition is not affected by this change. This paves the way for more accessible models that accelerate the statistical coupling of tire tread characteristics and their acoustic, handling or braking behavior.
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