Over-deposition assessment of Direct Energy Deposition (DED) using melt pool geometric features and Machine Learning

Over-deposition’s influence on the DED process is significant in terms of process stability and final part quality. Most recent studies on quality assessment are based on online monitoring of the melt pool and comparing it with pre-specified thresholds. In this study an online machine learning model is developed, capable of identifying two over-deposition defect types, satellite and wire-oscillation, by using frames captured by an optical camera targeting the melt pool. The features extracted from the images included five geometrical melt pool features and the operating laser power and were successfully used to achieve a 94.4% classification accuracy regardless of the final part geometry.