Tire Wear Life Prediction Using Machine Learning Technique Honda Motor Company, Limited

Format:

Book

Conference/Event

Author/Creator:

Ando, Takashi, author.

Conference Name:

WCX SAE World Congress Experience (2025-04-08 : Detroit, Michigan, United States)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2025

Summary:

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.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.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

Notes:

Vendor supplied data

Publisher Number:

2025-01-8757

Access Restriction:

Restricted for use by site license