Real-time defect detection using online learning for laser metal deposition

Abstract

Defect prevention and detection are very crucial for the quality improvement of additive manufacturing (AM) processes. Timely identification of imperfections and flaws in the manufactured products will allow effective and early implementation of corrective actions and thus impede the spread of defects to the whole industrial value chain. In laser metal deposition (LMD) AM processes, defects are imperfections which typically include “porosity” and “cracks formation”. This study presents novel data-driven anomaly detection techniques that use both transfer learning and online learning to assess the quality of melt pool images taken during the LMD process of a part. The proposed methods incorporate the characteristics and dynamics of the manufactured part during the printing process along with previous knowledge acquired from historical data. Continual online learning models (K-means and self-organizing maps) are developed whose parameters are adapted to the incoming data collected in real-time as a new part is being manufactured. The proposed models significantly outperformed the performance of their batch-learning counterparts in detecting anomalous melt pool images in an additively manufactured Ti–6Al–4V thin-walled part. Both models required an average of ∼0.07 s to process each incoming melt pool image, update their parameters and give a prediction on the image's health. This shows the potential of continual online learning for real-time anomaly detection.