Cabin noise modeling for seat location variation using AI and Machine Learning

Analyzing acoustic performance in large and complex assemblies, such as vehicle cabins, can be a time-intensive process, especially when considering the impact of seat location variations on noise levels. This paper explores the use of AI and machine learning to streamline this analysis by predicting the effects of different seat configurations on cabin noise, particularly at the driver’s ear level. The study begins by establishing a baseline simulation of cabin noise and generating training data for various seat location scenarios. This data is then used to train an AI model capable of predicting the noise impact of further seat adjustments. These predictions are validated through detailed simulations. The paper discusses the accuracy of these predictions, the challenges encountered, and provides insights into the effective use of AI and machine learning in acoustic analysis for cabin noise, with a specific emphasis on seat location as a key variable. Keywords: Acoustic analysis, AI, machine learning, cabin noise, simulation, Ansys.