In-suit monitoring melt pool states in direct energy deposition using ResNet

One critical challenge of directed energy deposition (DED) in additive manufacturing (AM) is the lack of comprehension of the relationship between the part parameters and the formation quality. Components fabricated by the inappropriate manufacturing parameters will be too unreliable to satisfy the strict requirements of industrial applications. To address these issues, the present study established an experiment with a coaxial high-speed charge coupled device (CCD) camera to monitor the 316L deposition process and developed a data-driven model with ResNet101 to identify different melt pool states. We adopted the t-distributed stochastic neighbor embedding clustering algorithm, accuracy rate, and normalized confusion matrix to evaluate the performance of ResNet101. Furthermore, the visualization technique class activation mapping was used to analyze the morphological characteristics of the melt pool formed under different experimental parameters, explained the classification behavior of the ResNet101 model. The result indicated that ResNet101 gains better feature extraction and higher capability to classify different melt pool states with an average accuracy of 99.07%, compared with other CNNs (LeNet, GoogLeNet, AlexNet, ResNet34, and ResNet50) models. This demonstrated the effectiveness of ResNet101 in monitoring the DED process and the potential to reduce fabrication costs in DED.