  This study aims to relate the time-frequency patterns of acoustic emission
(AE) and other multi-modal sensor data collected in a hybrid directed energy
deposition (DED) process to the pore formations at high spatial (0.5 mm) and
time (< 1ms) resolutions. Adapting an explainable AI method in LIME (Local
Interpretable Model-Agnostic Explanations), certain high-frequency waveform
signatures of AE are to be attributed to two major pathways for pore formation
in a DED process, namely, spatter events and insufficient fusion between
adjacent printing tracks from low heat input. This approach opens an exciting
possibility to predict, in real-time, the presence of a pore in every voxel
(0.5 mm in size) as they are printed, a major leap forward compared to prior
efforts. Synchronized multimodal sensor data including force, AE, vibration and
temperature were gathered while an SS316L material sample was printed and
subsequently machined. A deep convolution neural network classifier was used to
identify the presence of pores on a voxel surface based on time-frequency
patterns (spectrograms) of the sensor data collected during the process chain.
The results suggest signals collected during DED were more sensitive compared
to those from machining for detecting porosity in voxels (classification test
accuracy of 87%). The underlying explanations drawn from LIME analysis suggests
that energy captured in high frequency AE waveforms are 33% lower for porous
voxels indicating a relatively lower laser-material interaction in the melt
pool, and hence insufficient fusion and poor overlap between adjacent printing
tracks. The porous voxels for which spatter events were prevalent during
printing had about 27% higher energy contents in the high frequency AE band
compared to other porous voxels. These signatures from AE signal can further
the understanding of pore formation from spatter and insufficient fusion.
