  The authors present a generic framework for the parameter optimization of
additive manufacturing (AM) processes, one tailored to a high-throughput
experimental methodology (HTEM). Given the large number of parameters, which
impact the quality of AM-metallic components, the authors advocate for
partitioning the AM parameter set into stages (tiers), based on their relative
importance, modeling one tier at a time until successful, and then
systematically expanding the framework. The authors demonstrate how the
construction of multi-dimensional functions, based on neural networks (NN), can
be applied to successfully model relative densities and Rockwell hardness
obtained from HTEM testing of the Inconel 718 superalloy fabricated, using a
powder-bed approach. The authors analyze the input data set, assess its
suitability for predictions, and show how to optimize the framework for the
multi-dimensional functional construction, such as to obtain the highest degree
of fit with the input data set. The authors also compare and contrast the
NN-based multi-dimensional functional construction to multi-variate linear
regression, to polynomial regression, and to Gaussian process regression (GPR),
highlight similarity between the NN-based multi-dimensional functional
construction and the GPR, and offer insights into the suitability of each of
these methods for the data set and the application at hand. In terms of the
coefficient of determination, $R^2$, a relatively simple, single-layer NN with
5 or 10 nodes outperforms multi-variate linear regression, 2nd-order polynomial
regression, and GPR for the primary Inconel 718 HTEM data set studied. The
novelty of the research work entails the versatile and scalable NN framework
presented, suitable for use in conjunction with HTEM, for the AM parameter
optimization of superalloys, and beyond.
