The objective of this thesis is the targeted design of new wear resistant materials through the development of analytic frameworks. The building of databases on wear data, whether through calculation or experiment, is a very time-consuming problem with high levels of data uncertainty. For these reasons of small data size and high data uncertainty, the development of a hybrid data analytic framework for accelerating the selection of target materials is needed. In this thesis, the focus is on binary ceramic compounds with the properties of interest as friction coefficient and hardness and with the objective being to minimize friction while improving the wear resistance. These design requirements are generally inversely correlated, further requiring the data science framework that is developed in this thesis.

This thesis develops a new hybrid methodology of linking dimensionality reduction (principal component analysis) and association mining to aid in materials selection. The novelty in this developed approach is the linking of multiple data mining methodologies into a single framework, which addresses issues such as physically-meaningful attribute selection, addressing data uncertainty, and identifying specific candidate materials when property trade-offs exist. The result of this thesis is a hybrid methodology for material selection, which is used here for identifying new promising materials for wear resistant applications.