Tire Wear Life Prediction Using Machine Learning Technique

<div class="section abstract"><div class="htmlview paragraph">As global warming and environmental problems are becoming more serious, tires are required to achieve a high level of performance trade-offs, such as low rolling resistance, wet braking performance, driving stability, and ride comfort, while minimizing wear, noise, and weight. However, predicting tire wear life, which is influenced by both vehicle and tire characteristics, is technically challenging so practical prediction method has long been awaited.</div><div class="htmlview paragraph">Therefore, we propose an experimental-based tire wear life prediction method using measured tire characteristics and the wear volume formula of polymer materials. This method achieves practical accuracy for use in the early stages of vehicle development without the need for time-consuming and costly real vehicle tests. However, the need for improved quietness and compliance with dust regulations due to vehicle electrification requires more accuracy, leading to an increase in cases requiring judgment through real vehicle tests.</div><div class="htmlview paragraph">To address this technical issue, our new prediction model introduces vehicle characteristic factors that affect tire life, specifically the toe angle and camber angle of suspension alignment. Furthermore, since the contribution of each parameter varies depending on the tire mounting position and contact area, we introduced a machine learning technique to optimize our model.</div></div>