Realtime in-process monitoring of porosity during additive manufacturing

B. Zhang, S. Liu, Y.C. Shin
Purdue University,
United States

Keywords: additive manufacturing, porosity, in-process monitoring


This work describes realtime in-process porosity monitoring for additive manufacturing processes based on deep learning and a real time weld pool monitoring system. A high-speed digital camera was mounted coaxially to the laser beam for in-process sensing of melt-pool data, and deep learning convolutional neural network models were designed to learn melt-pool features to predict the porosity attributes in built specimens during additive manufacturing. The convolutional neural network (CNN) models with a compact architecture, part of whose hyper parameters were selected through cross-validation analysis, achieved a classification accuracy of 91.2% for porosity occurrence detection in the direct laser deposition of sponge Titanium powders and presented predictive capacity for micro pores below 100 ┬Ám. For local volume porosity prediction, the model also achieved a root mean square error of 1.32% and exhibited high fidelity for both high porosity and low porosity specimens. The system is low cost and can be easily outfitted on many commercial additive manufacturing systems.