Machine learning based in situ quality estimation by molten pool condition-quality relations modeling using experimental data. (arXiv:2103.12066v1 [cond-mat.mtrl-sci])

The advancement of machine learning promises the ability to accelerate the
adoption of new processes and property designs for metal additive
manufacturing. The molten pool geometry and molten pool temperature are the
significant indicators for the final part’s geometric shape and microstructural
properties for the Wire-feed laser direct energy deposition process. Thus, the
molten pool condition-property relations are of preliminary importance for in
situ quality assurance. To enable in situ quality monitoring of bead geometry
and characterization properties, we need to continuously monitor the sensor’s
data for molten pool dimensions and temperature for the Wire-feed laser
additive manufacturing (WLAM) system. We first develop a machine learning
convolutional neural network (CNN) model for establishing the correlations from
the measurable molten pool image and temperature data directly to the geometric
shape and microstructural properties. The multi-modality network receives both
the camera image and temperature measurement as inputs, yielding the
corresponding characterization properties of the final build part (e.g., fusion
zone depth, alpha lath thickness). The performance of the CNN model is compared
with the regression model as a baseline. The developed models enable molten
pool condition-quality relations mapping for building quantitative and
collaborative in situ quality estimation and assurance framework.



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