Thermal imaging-based diagnostic process using explainable artificial intelligence for 3D printing system

This paper proposes a thermal image-based diagnostic process using explainable artificial intelligence that can interpret 3D printer states. Through this process, it is possible to classify the status of the 3D printer, check the classified active area, and help determine the status of the equipment. First, the proposed methodology stores the thermal image through an acquisition system that visualizes the thermal image of the 3D printer. Second, training a convolution neural network (CNN) model is carried out by defining the state data for the thermal image. Finally, based on the CNN model, the proposed process classifies the thermal images of the 3D printer and visualizes the area that is the basis for the inference classification result using an explanatory artificial intelligence algorithm. Through the proposed process, the operator can check the 3D printers’ condition through the thermal image. By presenting the basis for inference the state, even unskilled operators can quickly check the state of the 3D printer.