Construction of Multi-Dimensional Functions for Optimization of Additive-Manufacturing Process Parameters

Abstract

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.