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.