Additive manufacturing (AM) is a crucial component of smart manufacturing systems
that disrupts traditional supply chains However, the parts built using the state-of-the-art
powder-bed 3D printers have noticeable unpredictable mechanical properties. In this
paper, we propose a closed-loop machine learning algorithm as a promising way of improving
the underlying failure phenomena in 3D metal printing. We employ machine learning
approach through a Deep Convolutional Neural Network to automatically detect the defects
in printing the layers, thereby turning metal 3D printers into essentially their own
inspectors. By comparing three deep learning models, we demonstrate that transfer
learning approach based on Inception-v3 model in Tensorflow framework can be used
to retrain our images data set consisting of only 200 image samples and achieves a
classification accuracy rate of 100 % on the test set. This will generate a precise
feedback signal for a smart 3D printer to recognize any issues with the build itself
and make proper adjustments and corrections without operator intervention. The closed-loop
ML algorithm can enhance the quality of the AM process, leading to manufacturing better
parts with fewer quality hiccups, limiting waste of time and materials.
