2022 Thayer Investiture

All Thayer Events

PhD Thesis Defense: Andrew Peterson

Jun

03

Thursday
11:30am - 12:30pm EST

Videoconference

For info on how to attend this videoconference, please email andrew.c.peterson.TH@dartmouth.edu

"Understanding the Deformation Behavior of Alumina-forming Austenitic Stainless Steels"

Abstract

Alumina-forming austenitic stainless steels (AFAs) are a class of steel under development for use in high temperature and corrosive conditions. AFAs show great promise to operate at these conditions, but the deformation behavior is poorly understood and this limits the optimization of the mechanical properties. This dissertation seeks to understand the deformation behavior of the model alloy Fe-20Cr-30Ni-2Nb-5Al.

First, the microstructural evolution was investigated during creep. Findings indicate that the creep rate is correlated with changes in the microstructure. Nano-sized L12-structured precipitates nucleating in the matrix reduced the creep rate, while at longer creep times coarsening of precipitates and formation of a wide precipitate free zone (PFZ) increased the creep rate.

Next, the formation mechanism, growth, and mechanical properties of the L12 PFZ were studied. It is shown that a PFZ forms due to solute depletion resulting in the dissolution of L12 precipitates. The growth of the PFZ can be modeled well with the diffusion of Ni through the matrix. Micro-cracks observed in the PFZ of crept samples indicate that the PFZ is softer than the surrounding matrix and ultimately compromises the creep strength.

High-temperature deformation mechanisms were then investigated using both creep tests and strain rate jump tests. Results confirm that the L12 precipitates significantly strengthen the matrix. Additionally, a grain boundary (GB) strengthening mechanism occurs in which the GB precipitates act as barriers to dislocation movement and prevent GB sliding.

Finally, machine learning and advanced data analytics techniques were applied to AFA creep data to gauge the prospects of using machine learning models to improve alloy design. While this approach shows promise for predicting creep properties of AFA alloys, it is not yet practical for alloy design without improving the dataset.

Thesis Committee

  • Ian Baker, PhD (Chair)
  • Harold Frost, PhD
  • Jifeng Liu, PhD
  • Michael Brady, PhD

Contact

For more information, contact Daryl Laware at daryl.a.laware@dartmouth.edu.