Beta-titanium alloys contain more elements that stabilize the
beta-titanium phase, such as Molybdenum, than traditional alloys like
Ti-6Al-4V. These alloying elements have several great benefits on an
industrial level; they slow down the hardening process in the alloy
and also lower the beta-transus temperature, which in turn lowers the
necessary hot rolling temperature. By lowering the hot-rolling
temperature, this not only saves money on the furnace internals, but
it also decreases the amount of material affected by oxygen.
These alloys, while not useful at elevated temperatures, offer great
combinations of strength, ductility, toughness and corrosion
resistance at near room temperature. Alloys such as
Ti-5Al-5Mo-5V-3Cr-0.4 Fe (Timetal 555 or Ti-5553) can be heat treated
to exceptional strength in sections up to six in inches thick, due to
the slow diffusion kinetics of the alloy. This allows the material to
be used in structural applications, where high overall strength and
high specific strength are both required.
Existing legacy information provides little understanding of how
microstructure affects fatigue life. It has been shown already that
microstructure impacts yield strength and other mechanical properties;
and it is also known that yield strength affects aspects of fatigue
life.
My current research is to explore the microstructural dependency of
fatigue life. To accomplish this, material is heat treated and tensile
tests performed to determine the yield strength. Then, four-point bend
fatigue tests are used to compare the total lifetime of the material.
The material is tested such that each test has a maximum stress equal
to 90% of the condition's yield strength. This ranks the fatigue life
relative to microstructural features while comparing with at a
constant fraction of plastic deformation.
To quantify the benefits of certain microstructural features, SEM
characterization tools and neural networks are used in this work.
These will provide a greater level of understanding for how
microstructure changes fatigue life in Ti-5553 than has ever been
realized for any beta-titanium alloy. Neural networks are used as
regression tools to provide insight to how each microstructural input
affects the fatigue life output. Virtual experiments can then be
designed to graphically interpret these dependencies, and subsequent
experiments designed to observe the underlying mechanisms involved.
John Foltz graduated from Michigan State University (MSU) in December 2005 with a BS in Materials Science. He completed a MS thesis entitled New Metallurgical Joining Methods for NiTi in Materials Science at MSU in August 2007. In September, 2007 he joined CAMM at The Ohio State University. He is currently working towards his PhD.
John is from Lansing, MI, and enjoys swimming, cooking, reading, teaching and working on his car.
During this BS and MS degrees John worked on the MSU Solar Car Team. As the vice-president, John coordinated weekly meetings, work on the car, purchasing and designing of the car. He also worked on the solar car, machining material for the frame and working on the carbon fiber body. Also during his BS and MS degrees, John helped put together events for the MSE Society as a representative for Symposium Day, which is an invitation based symposium that provides undergraduates with engineering knowledge not taught in the classroom . John also greatly enjoys teaching and was a tutor for hire when not teaching lab sections of the Introduction to Materials Science class at MSU.
