Data-Driven Multiscale Science for Tread Compounding

IF 0.9 Q4 ENGINEERING, MECHANICAL

Tire Science and Technology Pub Date : 2022-06-23 DOI:10.2346/tire.22.21003

Craig Burkhart, B. Jiang, G. Papakonstantopoulos, P. Polińska, Hongyi Xu, R. J. Sheridan, L. Brinson, W. Chen

引用次数: 0

Abstract

Tread compounding has always been faced with the simultaneous optimization of multiple performance properties, most of which have tradeoffs between the properties. The search for overcoming these conflicting tradeoffs have led many companies in the tire industry to discover and develop material physics-based platforms. This report describes some of our efforts to quantify compound structures and properties at multiple scales, and their subsequent application in compound design. Integration of experiment and simulation has been found to be critical to highlighting the levers in data-driven multiscale compound tread design.

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数据驱动的胎面复合多尺度科学

胎面复合一直面临着多种性能性能的同时优化，其中大多数性能都在性能之间进行权衡。为了克服这些相互矛盾的权衡，轮胎行业的许多公司发现并开发了基于材料物理的平台。本报告描述了我们在多个尺度上量化化合物结构和性能的一些努力，以及它们在化合物设计中的后续应用。实验和模拟的集成对于突出数据驱动的多尺度复合胎面设计中的杠杆至关重要。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Tire Science and Technology ENGINEERING, MECHANICAL-

CiteScore

2.10

自引率

0.00%

发文量

期刊介绍： Tire Science and Technology is the world"s leading technical journal dedicated to tires. The Editor publishes original contributions that address the development and application of experimental, analytical, or computational science in which the tire figures prominently. Review papers may also be published. The journal aims to assure its readers authoritative, critically reviewed articles and the authors accessibility of their work in the permanent literature. The journal is published quarterly by the Tire Society, Inc., an Ohio not-for-profit corporation whose objective is to increase and disseminate knowledge of the science and technology of tires.