In-Situ Quality Intelligent Classification of Additively Manufactured Parts Using a Multi-Sensor Fusion Based Melt Pool Monitoring System

Although laser powder bed fusion (LPBF) technology is considered one of the most promising additive manufacturing techniques, the fabricated parts still suffer from porosity defects, which can severely impact their mechanical performance. Monitoring the printing process using a variety of sensors to collect process signals can realize a comprehensive capture of the processing status; thus, the monitoring accuracy can be improved. However, existing multi-sensing signals are mainly optical and acoustic, and camera-based signals are mostly layer-wise images captured after printing, preventing real-time monitoring. This paper proposes a real-time melt-pool-based in-situ quality monitoring method for LPBF using multiple sensors. High-speed cameras, photodiodes, and microphones were used to collect signals during the experimental process. All three types of signals were transformed from one-dimensional time-domain signals into corresponding two-dimensional grayscale images, which enabled the capture of more localized features. Based on an improved LeNet-5 model and the weighted Dempster-Shafer evidence theory, single-sensor, dual-sensor and triple-sensor fusion monitoring models were investigated with the three types of signals, and their performances were compared. The results showed that the triple-sensor fusion monitoring model achieved the highest recognition accuracy, with accuracy rates of 97.98%, 92.63%, and 100% for high-, medium-, and low-quality samples, respectively. Hence, a multi-sensor fusion based melt pool monitoring system can improve the accuracy of quality monitoring in the LPBF process, which has the potential to reduce porosity defects. Finally, the experimental analysis demonstrates that the convolutional neural network proposed in this study has better classification accuracy compared to other machine learning models.