@article {2211,
	title = {Rapid Screening of Single Phase Refractory Alloys Under Laser Melting Conditions},
	journal = {Materials \& Design},
	year = {2024},
	pages = {112726},
	abstract = {<p>
	Refractory alloys can be difficult to fabricate by laser-based manufacturing methods due to their high melting temperatures, high interstitial solubility, and propensity for low temperature brittleness. Laser-based processes, such as welding and additive manufacturing (AM), yield similar populations of defects, including microsegregation and solidification and solid-state cracking. Given the extreme challenges and cost associated with the production of refractory powders, this research aimed to develop a rapid screening methodology that combines predictive defect formation metrics with single track melting experiments. A flexible single laser track melting platform was designed to perform screening experiments on conventional and multi-principal element refractory alloys across a wide range of laser energy inputs. The platform was employed to investigate laser melting on solid substrates, or on a substrate with a single layer of powder feedstock, and is demonstrated with the highly fabricable Nb-base alloy C103. Preliminary investigations are performed on refractory multi-principal element alloys in the Hf-Mo-Nb-Ta-Ti family, and significant differences in cracking resistance and solidification morphology are observed. Implications for future alloy design and processing strategies for defect-resistant refractory alloys for AM are discussed.
</p>
},
	keywords = {Additive manufacturing, high entropy alloy, multi-principal element alloy, refractory alloys, solid-state cracking, solidification cracking},
	issn = {0264-1275},
	doi = {https://doi.org/10.1016/j.matdes.2024.112726},
	url = {https://www.sciencedirect.com/science/article/pii/S0264127524000984},
	author = {Kaitlyn M. Mullin and Carolina Frey and James Lamb and Sophia K. Wu and McLean P. Echlin and Tresa M. Pollock}
}
@article {1741,
	title = {Bayesian inference of elastic constants and texture coefficients in additively manufactured cobalt-nickel superalloys using resonant ultrasound spectroscopy},
	journal = {Acta Materialia},
	volume = {220},
	year = {2021},
	pages = {117287},
	abstract = {<p>
	Bayesian inference with sequential Monte Carlo is used to quantify the orientation distribution function coefficients and to calculate the fully anisotropic elastic constants of additively manufactured specimens from only the experimentally-measured resonant frequencies. The parallelizable and open-source SMCPy Python package enabled Bayesian inference within this new modeling framework, resulting in an order of magnitude reduction of the computation time for an 8-core machine. Residual stress-induced shifts on the resonant frequencies were explicitly accounted for during the Bayesian inference, enabling the estimation of their effect on the resonant frequencies without a stress-relief heat treatment. Additively manufactured cobalt-nickel-base superalloy (SB-CoNi-10C) specimens were sectioned at multiple inclinations relative to the build direction and scanned with resonant ultrasound spectroscopy to demonstrate characterization of any arbitrarily textured cubic microstructure through the resonant frequencies. The orientation distribution function coefficients of the textured polycrystalline microstructure were estimated in tensorial form to calculate both the 2nd order Hashin-Shtrikman bounds and the self-consistent estimate of the elastic constants, enabling accurate determination of all 21 possible independent elastic constants through the convergence constraints of the texture. Pole figures generated directly from the calculated texture coefficients showed good agreement with experimentally measured textures.
</p>
},
	keywords = {Additive manufacturing, Bayesian inference, Parallel computing, Residual stress, Resonance ultrasound spectroscopy, Sequential monte carlo, Texture characterization},
	issn = {1359-6454},
	doi = {https://doi.org/10.1016/j.actamat.2021.117287},
	url = {https://www.sciencedirect.com/science/article/pii/S1359645421006674},
	author = {Jeff Rossin and Patrick Leser and Kira Pusch and Carolina Frey and Sean P. Murray and Chris J. Torbet and Stephen Smith and Samantha Daly and Tresa M. Pollock}
}
