Grain-scale microstructure evolution during additive manufacturing is a complex physical process. As with traditional solidification methods of material processing (e.g. casting and welding), microstructural properties are highly dependent on the solidification conditions involved. Additive manufacturing processes however, incorporate additional complexity such as remelting, and solid-state evolution caused by subsequent heat source passes and by holding the entire build at moderately high temperatures during a build. We present a three-dimensional model that simulates both solidification and solid-state evolution phenomena using stochastic Monte Carlo and Potts Monte Carlo methods. The model also incorporates a finite-difference based thermal conduction solver to create a fully integrated microstructural prediction tool. The three modeling methods and their coupling are described and demonstrated for a model study of laser powder-bed fusion of 300-series stainless steel. The investigation demonstrates a novel correlation between the mean number of remelting cycles experienced during a build, and the resulting columnar grain sizes.
Bias. It’s a word that makes most of us squirm. Bias implies to us that we are “bad people” and are being accused of deliberately discriminating against others. Yet, if you ask a social scientist, you will find that it doesn't mean that at all; implicit bias is a neurologically based, energy-saving short cut. Our brains apply mental models to make thousands of quick decisions every day: which brand of milk to buy at the store or when to turn the wheel to avoid a traffic accident. Lastly, we form our implicit biases subconsciously over time, influenced by our upbringing, societal norms, and life experiences.
This report summarizes the data analysis activities that were performed under the Born Qualified Grand Challenge Project from 2016 - 2018. It is meant to document the characterization of additively manufactured parts and processe s for this project as well as demonstrate and identify further analyses and data science that could be done relating material processes to microstructure to properties to performance.