Application of AI/ML in Hydrobush Tuning to Enhance Overall Value Proposition

In this intense market of the automotive industry, optimization of vehicle components for superior performance and customer satisfaction is paramount. Hydrobushes play an integral role in achieving this within vehicle suspension systems by absorbing vibrations and improving ride comfort. The methods traditionally used for tuning these components are not only time-consuming but also heavily reliant on extensive empirical testing. This paper explores the ever-growing field of artificial intelligence (AI) and machine learning (ML) in the hydrobush tuning process. Leveraging AI/ML algorithms such as random forest, artificial neural network, and logistic regression to analyse vast datasets, identify patterns, and predict optimal configurations more efficiently than conventional methods. The stated approach accelerates the tuning process while also enhancing the precision of the outcomes, leading to superior ride quality and durability. Thus, the integration of AI/ML techniques in hydrobush tuning offers significant cost savings, reduces development time, and improves the overall value proposition of automotive products. This paper presents a detailed examination of the methodologies, implementation strategies, and benefits of incorporating AI/ML in hydrobush tuning, backed by a case study. Keywords: Hydrobush, AI/ML, Optimization, Random Forest