The fatigue life of a material is the number of load cycles it can withstand before breaking; for example, the fatigue life of a straightened paperclip is the number of times it can be bent back-and-forth before it breaks. Aircrafts, automobiles, and biomedical devices are prone to such failures, yet many of the mechanisms that govern fatigue life are poorly understood. This understanding is especially limited for Nickel-Titanium alloys, which are used in such varied applications as biomedical devices and Mars rover tires. This award supports research into the cause of fatigue crack formation which eventually leads to material failure in a Nickel-Titanium material and will create a design tool for enhancing fatigue life. The result will benefit the healthcare field by increasing the robustness of artificial heart valves, stents, and other minimally invasive biomedical devices, therefore decreasing patient trauma and healthcare costs. This project also exposes graduate students to high-performance computing and Argonne National Laboratory's Advanced Photon Source. Skills gained at the Advanced Photon Source will position the student to contribute to national energy and defense needs. High-performance computing training will expand a workforce focused on big-data, machine learning, and artificial intelligence. Additionally, this project will familiarize students with engineering vocabulary by producing a video about fatigue that is intended to reduce barriers for first generation engineers and improve engineering education.
Superelastic Nickel-Titanium elastically recovers from large deformations and thus is ideal for minimally invasive biomedical devices and other applications such as Mars rover tires. However, due to microscale defects, Nickel-Titanium is prone to cyclic fatigue cracking. This project builds a crystal plasticity model of Nickel-Titanium and measures crack nucleation around a defect using X-ray micro-tomography and high energy diffraction microscopy. The measured defect geometry is then combined with the model to predict the plastic strain around the measured fatigue crack. Plastic strain plays a key role in fatigue crack nucleation but is difficult to measure; thus, a crystal plasticity model will be used. A data-driven procedure will automate the generation of a fatigue indicator parameter that predicts the mechanical state driving crack nucleation. Fatigue indicator parameters are a commonly proposed tool in the computational design of materials for fatigue resistance; however, current fatigue indicator parameters suffer from inaccuracies, which this project addresses. The projects outcomes are a validated fatigue indicator parameter-based modeling paradigm and a transformative design tool for the development of fatigue resistant superelastic Nickel-Titanium materials.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
