In situ monitoring of selective laser melting using plume and spatter signatures by deep belief networks.

Critical quality issues such as high porosity, cracks, and delamination are common in current selective laser melting (SLM) manufactured components. This study provides a flexible and integrated method for in situ process monitoring and melted state recognition during the SLM process, and it is useful for process optimization to decrease part quality issues. The part qualities are captured by images obtained from an off-axis setup with a near-infrared (NIR) camera. Plume and spatter signatures are closely related to the melted states and laser energy density, and they are employed for the SLM process monitoring in an adapted deep belief network (DBN) framework. The melted state recognition with the improved DBN and original NIR images requires little signal preprocessing, less parameter selection and feature extraction, obtaining the classification rate 83.40% for five melted states. Compared to the other methods of neural network (NN) and convolutional neural networks (CNN), the proposed DBN approach is identified to be accurate, convenient, and suitable for the SLM process monitoring and part quality recognition.