  Wire-feed laser additive manufacturing (WLAM) is gaining wide interest due to
its high level of automation, high deposition rates, and good quality of
printed parts. In-process monitoring and feedback controls that would reduce
the uncertainty in the quality of the material are in the early stages of
development. Machine learning promises the ability to accelerate the adoption
of new processes and property design in additive manufacturing by making
process-structure-property connections between process setting inputs and
material quality outcomes. The molten pool dimensional information and
temperature are the indicators for achieving the high quality of the build,
which can be directly controlled by processing parameters. For the purpose of
in situ quality control, the process parameters should be controlled in
real-time based on sensed information from the process, in particular the
molten pool. Thus, the molten pool-process relations are of preliminary
importance. This paper analyzes experimentally collected in situ sensing data
from the molten pool under a set of controlled process parameters in a WLAM
system. The variations in the steady-state and transient state of the molten
pool are presented with respect to the change of independent process
parameters. A multi-modality convolutional neural network (CNN) architecture is
proposed for predicting the control parameter directly from the measurable
molten pool sensor data for achieving desired geometric and microstructural
properties. Dropout and regularization are applied to the CNN architecture to
avoid the problem of overfitting. The results highlighted that the multi-modal
CNN, which receives temperature profile as an external feature to the features
extracted from the image data, has improved prediction performance compared to
the image-based uni-modality CNN approach.
