Rubber abrasion is one of the most important properties for rubber products, such as tyres. However, due to its complexity rubber abrasion is still a very challenging topic in rubber research. Rubber abrasion is not governed by a single mechanism. Different mechanisms can dominate the abrasion behaviour depending on the rubber compound, base polymer type, loading severity, contact conditions, testing temperature and chemical environment. This study investigates the different mechanisms for rubber abrasion and the transition between these mechanisms using two types of abrasion apparatus, a blade abrader and a surface abrader, respectively. Blade abrasion was used to generate the abrasion pattern. Once the abrasion pattern was formed on the rubber surface under unidirectional sliding, the underlying mechanism was primarily one of fatigue crack growth, which is referred as "fatigue wear" in the literature. An independent pure shear fatigue test with various loading profiles was conducted to predict the crack growth rate using a fracture mechanics approach during these abrasion tests. The tearing energy during blade abrasion was calculated using a fracture mechanics approach. A Finite Element Analysis (FEA) technique using the Virtual Crack Closure Technique (VCCT) was adopted. The VCCT approach was a simpler, faster and more reliable approach to derive the tearing energy under these complicated large strain contact conditions. The prediction of the abrasion rate using this independent measurement of the crack growth resistance of materials worked best for unfilled SBR material. A bespoke surface contact abrasion machine was used to investigate rubber abrasion on silicon carbide sandpaper under both dry and wet conditions. Depending on the materials, contact conditions and sliding velocity, two different mechanisms were observed. The first being a mechanochemical degradation, during which a sticky layer was generated on the rubber surface. This behaviour is also called "smearing wear". The second failure mode resulted from a purely mechanical fracture named "abrasive wear". It seemed that the carbon black filled rubber was more susceptible to smearing wear than the silica filled one. Higher sliding velocities promoted smearing wear, possibly due to higher temperatures being generated at the interface. Alternatively, water lubrication was seen to promote abrasive wear. Therefore, the abrasion mechanism changed to more rapid abrasive wear under wet conditions, which resulted in a significant increase in the rate of weight loss. Finally, the sticky debris generated during the smearing wear was characterised using various different techniques. This revealed that the sticky debris had more oxygen and lower carbon and sulphur content. It contained a greater amount volatiles and generated more char formation during its degradation in the air. The molecular weight of the sticky debris was much lower when compared to the original uncured rubber. It seemed that in the sticky debris the filler network can slowly recover and the degraded polymer chains can re-absorb back onto filler surface forming "bound rubber", which leads to faster rates of weight loss.

A major challenge in tire research, is tire wear modeling. There are too many factors affecting tire wear, and part of those factors are difficult to be accurately expressed in physics and math. The objective of this research is to develop a machine learning based rubber sample wear model, and find the correlation between sample wear and tire wear. To develop this model, accurate and diverse wear data is necessary. The Dynamic Friction Tester (DFT) was designed and built for this purpose. This test machine has made it possible to collect accurate rubber sample wear data which has been validated under different conditions. Wear tests under diverse test conditions were conducted, and the test data were used to train machine learned based wear models with different algorithms, such as Neural Networks and Support Vector Machines. With test-proved wear behavior classification as additional input, and feature selection, performance of the trained rubber sample wear model has been further improved. To correlate rubber sample wear and tire wear, a set of correlation functions were developed and proposed. By validating the correlation functions using tire wear test data collected on roads, this research contributes a fast and economical approach to predict tire wear. / Doctor of Philosophy / Tire wear is closely related to the life time of tire, and excessive wear of tire can results in serious accidents. Since 1950s, research have been done to predict tire wear using experiments and empirical relations. These approaches are expensive, time consuming, and highly restricted to certain conditions. The objectives of this research is to develop a statistic based rubber sample wear model, and find the correlation between rubber sample wear and tire wear. To develop the statistic based rubber sample wear model, a test machine, named Dynamic Friction Tester (DFT) was designed and built to collect rubber sample wear data. The final rubber sample wear model is trained by wear data under 600 different test conditions. A set of mathematical equations were proposed to correlate rubber sample wear and tire wear. These equations were validated by actual tire wear data collected from lab and public roads. In combination of the statistic based rubber sample wear model and mathematical relation between rubber sample wear and tire wear, this research contributes a flexible, economical, and fast method to predict tire wear.