Machine Learning Applications in the Development and Analysis of Suspension Systems of Road Vehicles

This paper aims to explore the application of machine learning techniques to the analysis of road suspension systems, with particular emphasis on mechanical leaf spring suspensions. These systems are essential for vehicle performance, as they guarantee comfort and stability while driving, and they have an intrinsically complex and non-linear dynamic behavior. Because of this complexity, traditional approaches often prove costly and insufficient to represent operating conditions. In this context, machine learning techniques stand out for their ability to learn patterns from experimental data, allowing the modelling of non-linear phenomena that characterize road implement suspensions. One of the main contributions of this study is the demonstration that machine learning algorithms are capable of identifying complex patterns to represent the behavior of the system, as well as facilitating the detection of anomalies and potential faults in the suspension system, contributing to predictive maintenance. The results indicate that the application of machine learning algorithms not only improves the accuracy of suspension performance analysis, but also offers an innovative approach to diagnosing and identifying problems. With the ability to process and analyze data in real-time, these technologies can be integrated into vehicle monitoring systems, allowing for quick and effective interventions. In conclusion, the use of machine learning in the analysis and design of road suspension systems represents a significant advance in automotive engineering. The research highlights the emergence of new research horizons in this area, suggesting that the combination of engineering knowledge and artificial intelligence can open up new frontiers for the development of more efficient and safer suspension systems.