  The extreme and repeated temperature variation during additive manufacturing
of metal parts has a large effect on the resulting material microstructure and
properties. The ability to accurately predict this temperature field in detail,
and relate it quantitatively to structure and properties, is a key step in
predicting part performance and optimizing process design. In this work, a
finite element simulation of the Directed Energy Deposition (DED) process is
used to predict the space- and time-dependent temperature field during the
multi-layer build process for Inconel 718 walls. The thermal model is validated
using the dynamic infrared (IR) images captured in situ during the DED builds,
showing good agreement with experimental measurements. The relationship between
predicted cooling rate, microstructural features, and mechanical properties is
examined, and cooling rate alone is found to be insufficient in giving
quantitative property predictions. Because machine learning offers an efficient
way to identify important features from series data, we apply a 1D
convolutional neural network (CNN) data-driven framework to automatically
extract the dominant predictive features from simulated temperature history.
The relationship between the CNN-extracted features and the mechanical
properties is studied. To interpret how CNN performs in intermediate layers, we
visualize the extracted features produced on each convolutional layer by a
trained CNN. Our results show that the results predicted by the CNN agree well
with experimental measurements and give insights into physical mechanisms of
microstructure evolution and mechanical properties.
