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.

During normal walking, the relative motion of the human knee involves flexion/extension, anterior/posterior sliding, and medial/lateral rotation. As well, the knee experiences a complex, dynamic loading curve with a peak of up to seven times body weight. However, most wear testing machines that have been used to evaluate total knee replacement materials are unidirectional and/or apply only static force. This thesis presents an alternate wear testing device capable of simulating the most prevalent motions of the knee, and applying physiologically-correct loading to the material interface. By incorporating a CoCr disc, an UHMWPE block, stepping motors, pneumatic components, computer control, and linear tables in an x-y configuration, the device is capable of quickly screening new and alternative materials to UHMWPE before evaluating them on a much more expensive knee simulator. In addition, flexibility of the device allows programming of many different motion and loading configurations permitting materials testing under only certain circumstances, or evaluating the effects on wear of specific motions. Design rationale, development, validation, and future recommendations are presented. / Master of Science

published_or_final_version / Mechanical Engineering / Master / Master of Science in Engineering

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