  Wire-feed Laser-based Additive Manufacturing has gained attention for years
with its promises of high-level automation, reducing materials waste, overall
costs, and large-scale volume production. However, comprehensive quality
quantification and control are difficult to implement due to the limitation of
dimensionality and cost of experimental data that traditional trial and error
methods and computational methods can handle. To enable quality control of
desired part geometry and microstructural properties of the part, this study
proposes a comprehensive quality investigation framework based on learning from
experimental data. Specifically, experimental data across various process
variables and output properties in terms of bead overall quality, geometry
(i.g. bead height, fusion zone depth, etc.), and microstructural properties are
collected. The predictive process-property relations are then modeled with
data-driven ML classification and regression models. The multiple properties of
the printed bead are predicted on processing space based on a 3-dimensional
contour map. The insights and impacts of process variables on bead morphology,
geometric and microstructural properties are comprehensively investigated. The
processing parameters leaned by the models can rapidly designing materials with
desired functional properties.
