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The extreme and repeated temperature variation during additive manufacturingof metal parts has a large effect on the resulting material microstructure andproperties. The ability to accurately predict this temperature field in detail,and relate it quantitatively to structure and properties, is a key step inpredicting part performance and optimizing process design. In this work, afinite element simulation of the Directed Energy Deposition (DED) process isused to predict the space- and time-dependent temperature field during themulti-layer build process for Inconel 718 walls. The thermal model is validatedusing the dynamic infrared (IR) images captured in situ during the DED builds,showing good agreement with experimental measurements. The relationship betweenpredicted cooling rate, microstructural features, and mechanical properties isexamined, and cooling rate alone is found to be insufficient in givingquantitative property predictions. Because machine learning offers an efficientway to identify important features from series data, we apply a 1Dconvolutional neural network (CNN) data-driven framework to automaticallyextract the dominant predictive features from simulated temperature history.The relationship between the CNN-extracted features and the mechanicalproperties is studied. To interpret how CNN performs in intermediate layers, wevisualize the extracted features produced on each convolutional layer by atrained CNN. Our results show that the results predicted by the CNN agree wellwith experimental measurements and give insights into physical mechanisms ofmicrostructure evolution and mechanical properties.

Data driven analysis of thermal simulations, microstructure and  mechanical properties of Inconel 718 thin walls deposited by metal additive  manufacturing