• Title/Summary/Keyword: 트레드 디자인

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The Study on the Characteristics of Groove Wandering and Noise Caused by the Design Parameter of Longitudinal Groove on the Tire's Pattern (타이어 패턴의 종그루브 인자에 따른 그루브 원더링 및 소음 특성 연구)

  • Han, Jin-Hyuk;Hwang, Sung-Wook;Kang, Yong-Gu;Han, Min-Hyeon
    • Journal of the Korean Society of Manufacturing Process Engineers
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    • v.10 no.5
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    • pp.110-116
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    • 2011
  • Water on a road surface can lower the coefficient of friction of vehicle tires and it involves lots of risks. One way to prevent water from staying is to cut many "rain grooves" into the edges of the pavement parallel. Such grooves, however, can exert unwanted side forces, particularly on passenger tires, which cause "Groove Wander" making the drivers uncomfortable. The "Groove Wander" is somewhat related to vehicle geometry, but is more strongly related to interaction between road grooves and tire tread's longitudinal grooves. This thesis tries to examine principles to estimate "Groove Wandering" effect focusing on the design of longitudinal groove. Additionally, it studied how to reduce pipe resonance longitudinal grooves form in the contact patch.

Non-pneumatic Tire Design System based on Generative Adversarial Networks (적대적 생성 신경망 기반 비공기압 타이어 디자인 시스템)

  • JuYong Seong;Hyunjun Lee;Sungchul Lee
    • Journal of Platform Technology
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    • v.11 no.6
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    • pp.34-46
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    • 2023
  • The design of non-pneumatic tires, which are created by filling the space between the wheel and the tread with elastomeric compounds or polygonal spokes, has become an important research topic in the automotive and aerospace industries. In this study, a system was designed for the design of non-pneumatic tires through the implementation of a generative adversarial network. We specifically examined factors that could impact the design, including the type of non-pneumatic tire, its intended usage environment, manufacturing techniques, distinctions from pneumatic tires, and how spoke design affects load distribution. Using OpenCV, various shapes and spoke configurations were generated as images, and a GAN model was trained on the projected GANs to generate shapes and spokes for non-pneumatic tire designs. The designed non-pneumatic tires were labeled as available or not, and a Vision Transformer image classification AI model was trained on these labels for classification purposes. Evaluation of the classification model show convergence to a near-zero loss and a 99% accuracy rate confirming the generation of non-pneumatic tire designs.

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