In situ quality monitoring in direct energy deposition process using co-axial process zone imaging and deep contrastive learning

Many strategic industrial sectors prefer Directed Energy Deposition (DED) to other Additive Manufacturing (AM) technologies due to the high material deposition and build rates. However, the inadvertent formation of defects such as porosity, micro-cracks and microstructure anomalies hinders its adoption in industries that require specific mechanical and microstructural properties. These defects are caused by undesirable fluctuations in process conditions such as material flow rate, laser power, melt pool dynamics, environment gas composition, temperature gradients. This research proposes in situ quality monitoring of DED using images of process zone and contrastive learning-based Convolutional Neural Network (CNN). Experiments included deposition of titanium powder (Cp-Ti, grade 1) with the particle size ranging between 45 and 106 μm on the base plate (99.6 % Ti6Al4V grade 1), forming a cube geometry. The process parameters were tuned to achieve six quality grades. The video of the process zone was recorded co-axially to the laser beam during the entire manufacturing, which was eventually used as the input to train CNN's based on contrastive losses. An in situ monitoring strategy for classifying the different quality grades was demonstrated in a supervised and semi-supervised manner, with an accuracy ranging between 89 % and 97 %. The performance of the developed framework was compared to an alternative clustering technique, namely t-distributed stochastic neighbour embedding, justifying the efficiency of our approach. The developed methodology demonstrates the possibility to track workpiece manufacturing quality using simple CCD cameras with minimum interventions on the commercial machines.