Detecting anomalies in aerospace additive manufacturing: A Convolutional Long Short-term Memory approach
As additive manufacturing technology within the aerospace sector advances rapidly, optimizing process monitoring methods has become increasingly critical, given that existing techniques remain costly and time-consuming. This study presents a novel approach to monitoring the laser metal deposition (LMD) process, emphasizing real-time anomaly detection through the analysis and prediction of frames f
