A Ni/TiC/C metal matrix composite (MMC) has been processed using the laser engineered net shaping (LENS) process from commercially available powders with a Ni-3Ti-20C (atomic %) composition. This processing route produces the in-situ formation of homogeneously distributed eutectic and primary titanium carbide and graphite precipitates throughout the Ni matrix. The composite exhibits promising tribological properties when tested in dry sliding conditions with a low steady state coefficient of friction (CoF) of ~0.1 and lower wear rates in comparison to LENS deposited pure Ni. The as deposited and tribologically worn composite has been characterized using Auger electron spectroscopy, scanning electron microscopy (SEM), X-ray diffraction, high resolution transmission electron microscopy (HRTEM) with energy dispersive spectroscopy (EDS), dual beam focused ion beam SEM (FIB/SEM) serial sectioning and Vickers micro-hardness testing. The evolution of subsurface stress states and precipitate motion during repeated sliding contact has been investigated using finite element analysis (FEA). The results of FIB/SEM serial sectioning, HRTEM, and Auger electron spectroscopy in conjunction with FEA simulations reveal that the improved tribological behavior is due to the in-situ formation of a low interfacial shear strength amorphous carbon tribofilm that is extruded to the surface via refined Ni grain boundaries.

Laser surface modification involves rapid melting and solidification is an elegant technique used for locally tailoring the surface morphology of alumina in order to enhance its abrasive characteristics. COMSOL Multiphysics® based heat transfer modeling and experimental approaches were designed and used in this study for single and multiple laser tracks to achieve densely-packed multi-facet grains via temperature history, cooling rate, solidification, scanning electron micrographs, and wear rate. Multi-facet grains were produced at the center of laser track with primary dendrites extending toward the edge of single laser track. The multiple laser tracks study indicates the grain/dendrite size increases as the laser energy density increases resulting in multiplying the abrasive edges which in turn enhance the abrasive qualities.

It is highly desirable to be able to make predictions of properties in metallic materials based upon the composition of the material and the microstructure. Unfortunately, the complexity of real, multi-component, multi-phase engineering alloys makes the provision of constituent-based (i.e., composition or microstructure) phenomenological equations extremely difficult. Due to these difficulties, qualitative predictions are frequently used to study the influence of microstructure or composition on the properties. Neural networks were used as a tool to get a quantitative model from a database. However, the developed model is not a phenomenological model. In this study, a new method based upon the integration of three separate modeling approaches, specifically artificial neural networks, genetic algorithms, and monte carlo was proposed. These three methods, when coupled in the manner described in this study, allows for the extraction of phenomenological equations with a concurrent analysis of uncertainty. This approach has been applied to a multi-component, multi-phase microstructure exhibiting phases with varying spatial and morphological distributions. Specifically, this approach has been applied to derive a phenomenological equation for the prediction of yield strength in a+b processed Ti-6-4. The equation is consistent with not only the current dataset but also, where available, the limited information regarding certain …