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Ultrasonic transducers are widely used in automotive and industrial applications for surround sensing. Anisotropic directivity patterns with a narrow-angled beam in the vertical plane and a wide-angled beam in the horizontal plane are needed in automotive applications particularly. Today’s ultrasonic transducers for automotive applications are mainly metal based, pot-like ultrasonic transducers. The anisotropic directivity pattern is achieved by increasing the thickness of the vibrating plate-like part of the structure locally. Composites with locally structured fiber reinforcements open up the possibility to design the dynamical behavior of components without changing its contour. Using this new dimension of design to modify the directivity pattern of sound radiating components is less examined in literature.

Computational models directly derived from data gained increased interest in recent years. Data-driven approaches have brought breakthroughs in different research areas such as image-, video- and audio-processing. Often denoted as Machine Learning (ML), today these approaches are not widely applied in the field of vehicle Noise, Vibration and Harshness (NVH). Works combining ML and NVH mainly discuss the topic with respect to psychoacoustics, traffic noise, structural health monitoring and as improvement to existing numerical simulation methods. Vehicle interior noise is a major quality criterion for today’s automotive customers. To estimate noise levels early in the development process, deterministic system descriptions are created by utilizing time-consuming measurement techniques. This paper examines whether pattern-recognizing algorithms are suitable to conduct the prediction process for a steering system.