Profile

Zach Brunson is a Research Engineer in the G. W. Woodruff School of Mechanical Engineering at the Georgia Institute of Technology, working primarily at the Advanced Manufacturing Pilot Facility (AMPF). Prior to Georgia Tech, Zach was a graduate teaching fellow and research assistant at the Colorado School of Mines in Golden Colorado where he received his Ph.D. (2021) and M.S. (2019) in Mechanical Engineering studying theoretical and experimental mechanics of inelastic anisotropic and asymmetric materials. Prior to pursuing a graduate degree, Zach gained experience working as a measurements field engineer in the petroleum industry (2013-2015) after earning his B.S. (2013) in Mechanical Engineering from the University of Colorado in Boulder Colorado.

Zach’s research revolves around as manufactured material property prediction, measurement, and certification. The two major thrusts of his research are: (1) theoretical and experimental mechanics of inelastic anisotropic and asymmetric materials and (2) sensor development for process monitoring and part qualification in directed energy deposition (DED) additive manufacturing (AM) systems. By developing a more complete understanding of the elastic limits of anisotropic and asymmetric materials, we can better describe both the deformation during manufacturing processes such as forging, forming, or rolling and the final strength of as manufactured (conventionally or AM) components. By developing sensor systems to monitor AM processes such as DED, we can begin to better inform the creation of predictive models, identify critical events related to part performance, improve feedback controls for more reliability and repeatability, and ultimately qualify processes and certify components.

Education

  • Ph.D., Colorado School of Mines, 2021
  • M.S., Colorado School of Mines, 2019
  • B.S., University of Colorado, 2013

Publications

Brunson, Zach D., et al. "An Expanded Martensite Variant Selection Theory Accounting for Transformation Rotations and Applied Stress Fields: Predictions of Variant Clusters in Titanium." JOM 72.10 (2020): 3594-3607.