Deep Generative Tread Pattern Design Framework for Efficient Conceptual Design

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Tire tread patterns have played an important role in the automotive industry because they directly affect automobile performances. The conventional tread pattern development process has successfully produced and manufactured many tire tread patterns. However, a conceptual design process, which is a major part of the whole process, is still time-consuming due to repetitive manual interaction works between designers and engineers. In the worst case, the whole design process must be performed again from the beginning to obtain the required results. In this study, a deep generative tread pattern design framework is proposed to automatically generate various tread patterns satisfying the target tire performances in the conceptual design process. The main concept of the proposed method is that desired tread patterns are obtained through optimization based on integrated functions, which combine generative models and tire performance evaluation functions. To strengthen the effectiveness of the proposed framework, suitable image pre-processing, generative adversarial networks (GANs), two-dimensional (2D) image-based tire performance evaluation functions, design generation, design exploration, and image post-processing methods are proposed with the help of domain knowledge of the tread pattern. The numerical results show that the proposed automatic design framework successfully creates various tread patterns satisfying the target tire performances such as summer, winter, or all-season patterns.
Publisher
ASME
Issue Date
2022-07
Language
English
Article Type
Article
Citation

JOURNAL OF MECHANICAL DESIGN, v.144, no.7

ISSN
1050-0472
DOI
10.1115/1.4053469
URI
http://hdl.handle.net/10203/292518
Appears in Collection
ME-Journal Papers(저널논문)
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