The quality of Inconel 718 (IN718) from selective laser melting (SLM) is prerequisite for its application, and meeting required tensile properties is particularly important. This study aims to realize both mechanical property prediction and process parameter selection of SLM-ed IN718 by taking full advantage of their process-tensile property data mined from literature.
Extensive data of interest are mined from literature, among which the missing data are then imputed by fitting Gaussian mixture model via expectation maximization. Forward/backward predictive models for predicting the unknowns in tensile properties (ultimate tensile strength, yield strength, elongation) along horizontal and vertical directions and key process parameters (laser power, scanning speed, hatch spacing, layer thickness) are built through Bayesian network.
None of the experiments from literature has complete data of the four key process parameters and three tensile properties along two directions. Satisfactory accuracies are obtained for both data imputation for the missing values in the mined literature data with an average R2 of 0.64 and forward/backward prediction of process-tensile property with an average R2 of 0.58/0.54. The data imputation and predictive models are also tested with consistent prediction accuracies.
Forward/backward process-tensile property predictive models of SLM-ed IN718 with satisfactory performance can be obtained after data imputation for the mined literature data. Such models consider more process parameters (four key process parameters) and properties (three tensile properties along two directions), which also cover wider ranges than any individual studies through a less costly while effective approach.
