During the early stages of developing winter and all-season tread compounds, it is essential to accurately predict their snow performance in the laboratory phase. This enables the proper selection of the most promising candidates for tire prototyping, which will later undergo outdoor testing to assess real-world snow performance.
Given the high costs associated with outdoor snow testing and the increasing impact of climate change, resulting in inconsistent temperatures and snow conditions that often fall outside acceptable testing parameters, accurate laboratory-based prediction methods have become even more critical.
The current laboratory method for snow performance prediction, which relies on Dynamic Mechanical Analysis (DMA), requires tread compounds to be heavily optimized for snow performance to ensure compliance with regulatory snow tests. However, this often leads to overengineering for snow conditions, potentially compromising other key performance areas, such as dry handling.
Therefore, there is a need for a more sensitive and balanced laboratory method for snow performance prediction; one that enables accurate evaluation without unnecessarily sacrificing other performance characteristics.
The aim of this project is to develop and validate a sensitive laboratory method for predicting the snow performance of winter and all-season tread compounds. The key objectives include:
- Development of a laboratory method
- Achieving a snow performance prediction accuracy of 95%
- Ensuring high precision of the method in terms of both repeatability and reproducibility
- The accuracy of the new laboratory method will be validated against the snow performance results of winter and all-season tires obtained through outdoor testing.