Advantages of Hybrid Neural Network Architectures to Enhance Prediction of Tensile Properties in Laser Powder Bed Fusion

Abstract

The properties of AlSi10Mg produced by Laser Powder Bed Fusion (PBF-LB) are defined by a multitude of different machine and laser parameters. This multi-parameter space presents the challenge of optimizing the material properties for a given application by the sheer amount of possible parameter combinations. Characterizing this multi-parameter space empirically is limited by time and resources and thus yields an incomplete picture of the process capabilities and local optima, respectively. To improve on this situation, machine learning to map the process parameters on the tensile properties of AlSi10Mg was used. The Hybrid Neural Network (HNN) used in this study consisted of a Convolutional Neural Network (CNN) to process the micrographs and a Dense Neural Network (DNN) to process the LPBF process parameters as well as the output of the CNN. The micrographs given to the CNN part of the network were printed with the same parameters given to the DNN part to include the information of the bulk microstructure as it strongly influences the tensile properties of the material. With the HNN, we observed good accuracy of the predicted tensile properties, given the small amount of training data. Furthermore, we explore which features of the micrographs were extracted by the CNN.