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Summary & Details

A Machine Learning Approach to Estimate Tire Block Force Spectrum for Road Noise Simulation

Delivery: Available on this site

Format: PDF

Price: Non-members (tax incl.)：￥1,100 Members (tax incl.)：￥880

Publication code: 20265026

Paper/Info type: Proceedings (Spring)

No.6-26

Pages: 1-5(Total 5 p)

Date of publication: May 2026

Publisher: JSAE

Language: English

Event: 2026 JSAE Annual Congress (Spring)

Detailed Information

Author(J)	1) Yonghun Kim, 2) Taeyoung Kim, 3) Hyunseok Kang
Author(E)	1) Yonghun Kim, 2) Taeyoung Kim, 3) Hyunseok Kang
Affiliation(J)	1) Hankook Tire, 2) Hankook Tire, 3) Hankook Tire
Affiliation(E)	1) Hankook Tire, 2) Hankook Tire, 3) Hankook Tire
Abstract(E)	To predict vehicle road noise using the Frequency Based Sub-structuring (FBS) method or to operate a virtual noise simulator, accurate tire block forces are essential. Prior to manufacturing a physical tire, these forces are commonly estimated through finite-element (FE) analysis; however, FE simulations typically require several days, making it difficult to rapidly assess the impact of design modifications on road-noise performance. To overcome this limitation, this study proposes a data-driven modeling framework capable of predicting the tire block-force spectrum directly from design specifications. The methodology, including feature construction, model training, and validation procedures, is described in detail, and the applicability of the proposed approach is demonstrated through prediction results.

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