The pre-trained model was tested on a 3D printer monitoring system for its ability to recognize the “spaghetti-shape-error” and was able to detect 96% of abnormal deposition processes. Sample images were trained based on a modified Visual Geometry Group Network (VGGNet) model and the trained model was evaluated, resulting in 97% accuracy. The webcam captures images and then analyzes them by machine learning based on a convolutional neural network (CNN), showing outstanding performance in both image classification and the recognition of objects. In this research, a failure detection method which uses a webcam and deep learning is developed for the ME process. In order to prevent this, the user must constantly monitor the process. Once occurring, this issue, which consumes both time and materials, requires a restart of the entire process. However, the error known as the “spaghetti-shape error,” related to filament tangling, is a common problem associated with the ME process. The material extrusion (ME) process is one of the most widely used 3D printing processes, especially considering its use of inexpensive materials.
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