Matching Items (2)
135315-Thumbnail Image.png
Description
The goal of this research is to compare the mechanical properties of CP-Ti and Ti-O and to understand the relationship between a material's microstructure and its response to fatigue. Titanium has been selected due to its desirable properties and applicability in several engineering fields. Both samples are polished and etched

The goal of this research is to compare the mechanical properties of CP-Ti and Ti-O and to understand the relationship between a material's microstructure and its response to fatigue. Titanium has been selected due to its desirable properties and applicability in several engineering fields. Both samples are polished and etched in order to visualize and characterize the microstructure and its features. The samples then undergo strain-controlled fatigue tests for several thousand cycles. Throughout testing, images of the samples are taken at zero and maximum load for DIC analysis. The DIC results can be used to study the local strains of the samples. The DIC analysis performed on the CP-Ti sample and presented in this study will be used to understand how the addition of oxygen in the Ti-O impacts fatigue response. The outcome of this research can be used to develop long-lasting, high strength materials.
ContributorsRiley, Erin Ashland (Author) / Solanki, Kiran (Thesis director) / Oswald, Jay (Committee member) / School of Art (Contributor) / Mechanical and Aerospace Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
147768-Thumbnail Image.png
Description

Procedural content generation refers to the creation of data algorithmically using controlled randomness. These algorithms can be used to generate complex environments and geological formations as opposed to manually creating environments, using photogrammetry, or other means. Geological formations and the surrounding terrain can be created using noise based algorithms such

Procedural content generation refers to the creation of data algorithmically using controlled randomness. These algorithms can be used to generate complex environments and geological formations as opposed to manually creating environments, using photogrammetry, or other means. Geological formations and the surrounding terrain can be created using noise based algorithms such as Perlin noise. However, interpreting noise in this manner has a number of challenges due to the pseudo-random nature of noise. We will discuss how to generate noise, how to render noise, and the challenges in interpreting noise.

ContributorsLi, Michael L (Author) / Hansford, Dianne (Thesis director) / Kobayashi, Yoshihiro (Committee member) / Computer Science and Engineering Program (Contributor, Contributor) / School of Art (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05