A method for melt pool state monitoring in laser-based direct energy deposition based on DenseNet

Abstract

Detecting and classifying the melt pool states in laser-based direct energy deposition (L-DED) is crucial for reducing defects and enhancing the mechanical properties of L-DED metal parts. Although physics-based modeling methods and traditional machine learning algorithms such as convolutional neural network have been introduced to monitor the melt pool states, improving the low accuracy of these methods remains to be challenging. To address this issue, we developed a DenseNet-39 model to classify the melt pool states. 80 single-track samples were fabricated using a linear scan strategy by L-DED and using a coaxial high-speed camera to capture the melt pool images in-process. Experimental results have demonstrated the superior performance of DenseNet-39 in classifying the melt pool states with 99.3% accuracy, achieved a lower computation burden, and less processing time. We used CAM to explain the mechanism of classification by DenseNet-39. DenseNet-39 provides the potential applications of online process monitoring in L-DED.