  In additive manufacturing, the optimal processing conditions need to be
determined to fabricate porosity-free parts. For this purpose, the design space
for an arbitrary alloy needs to be scoped and analyzed to identify the areas of
defects for different laser power-scan speed combinations and can be visualized
using a printability map. Constructing printability maps is typically a costly
process due to the involvement of experiments, which restricts their
application in high-throughput product design. To reduce the cost and effort of
constructing printability maps, a fully computational framework is introduced
in this work. The framework combines CALPHAD models and a reduced-order model
to predict material properties. THen, an analytical thermal model, known as the
Eagar-Tsai model, utilizes some of these materials' properties to calculate the
melt pool geometry during the AM processes. In the end, printability maps are
constructed using material properties, melt pool dimensions, and commonly used
criteria for lack of fusion, balling, and keyholing defects. To validate the
framework and its general application to laser powder-bed fusion alloys, five
common additive manufacturing alloys are analyzed. Furthermore, NiTi-based
alloys at three different compositions are evaluated to show the further
extension of the framework to alloy systems at different compositions. The
defect regions in these printability maps are validated with corresponding
experimental observations to compare and benchmark the defect criteria and find
the optimal criterion set with the maximum accuracy for each unique material
composition. Furthermore, printability maps for NiTi that are obtained from our
framework are used in conjunction with process maps resulting from a
multi-model framework to guide the fabrication of defect-free additive
manufactured parts with tailorable properties and performance.
