  Variation in the local thermal history during the laser powder bed fusion
(LPBF) process in additive manufacturing (AM) can cause microporosity defects.
in-situ sensing has been proposed to monitor the AM process to minimize
defects, but the success requires establishing a quantitative relationship
between the sensing data and the porosity, which is especially challenging for
a large number of variables and computationally costly. In this work, we
develop machine learning (ML) models that can use in-situ thermographic data to
predict the microporosity of LPBF stainless steel materials. This work
considers two identified key features from the thermal histories: the time
above the apparent melting threshold (/tau) and the maximum radiance (T_{max}).
These features are computed, stored for each voxel in the built material, are
used as inputs. The binary state of each voxel, either defective or normal, is
the output. Different ML models are trained and tested for the binary
classification task. In addition to using the thermal features of each voxel to
predict its own state, the thermal features of neighboring voxels are also
included as inputs. This is shown to improve the prediction accuracy, which is
consistent with thermal transport physics around each voxel contributing to its
final state. Among the models trained, the F1 scores on test sets reach above
0.96 for random forests. Feature importance analysis based on the ML models
shows that T_{max}is more important to the voxel state than /tau. The analysis
also finds that the thermal history of the voxels above the present voxel is
more influential than those beneath it.
