Data-Driven Multiscale Science for Tire Compounding: Methods and Future Directions

Hongyi Xu, Richard J. Sheridan, L. Catherine Brinson, Wei Chen, Bing Jiang, George Papakonstantopoulos, Patrycja Polinska, Craig Burkhart*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Modern tire compound design is confronted with the simultaneous optimization of multiple performance properties, most of which have tradeoffs between the properties. In order to uncover new design principles to overcome these historical tradeoffs, multiscale compound experiment, physics, and simulation are being developed and integrated into next-generation design platforms across the tire industry. This chapter describes the efforts in our laboratories to quantify compound structures and properties at multiple scales—from nanometers to microns—and their application in compound simulations. This integration of experiment and simulation has been found to be critical to highlighting the levers in data-driven multiscale compound design. We also provide a glimpse into the next set of capabilities, particularly from data science, which will impact future compound design.

Original languageEnglish (US)
Title of host publicationSpringer Series in Materials Science
PublisherSpringer Science and Business Media Deutschland GmbH
Pages281-312
Number of pages32
DOIs
StatePublished - 2021

Publication series

NameSpringer Series in Materials Science
Volume310
ISSN (Print)0933-033X
ISSN (Electronic)2196-2812

ASJC Scopus subject areas

  • Materials Science(all)

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