Data-driven material development
The tire industry has accumulated vast amounts of data and expertise on rubber compounds, their compositions/properties and how these relate to tire performance. Many industries are employing data-driven approaches such as machine learning (ML) and artificial intelligence (AI) for the development and application of new materials. The benefits of using ML/AI include accelerating the pace of development by reducing the volume of experimental testing needed and using the tools to suggest compound formulas that achieve design targets. This presentation will discuss some of the unique opportunities and challenges for data-driven material development in the tire and rubber industry.