Guiying Chen

Laser directed energy deposition (L-DED) has emerged as a promising technique for rapid prototyping due to its cost-effectiveness and efficiency. However, the intricate and multi-scale physics of the process hinder its widespread application. This paper addresses the challenge by focusing on real-time identification of melt pool states to detect defects early and minimize resource wastage. To achieve this, a FixConvNeXt model was developed for fast and accurate monitoring of melt pool states. This model was trained using 5000 melt pool images captured during the printing of single-track deposits from a charge-coupled device. To evaluate its performance, FixConvNeXt was compared with other models using various metrics. Experimental results demonstrated that FixConvNeXt achieved superior performance in accurately identifying melt pool states with 99.1% accuracy, while also reducing computation burden and processing time. The mechanism of classification by FixConvNeXt was explained using gradient-weighted class activation mapping. The research findings highlight the potential application of online process monitoring in L-DED. This study lays the foundation for future development of an efficient deep learning network for automatic defect detection and feedback control.