Deep Learning-Based 3D Printer Fault Detection

Abstract

The development of intelligent manufacturing and 3D printers is rapidly engaging in the industry. However, 3D printers are challenged by occasional anomalies due to leading to failure in 3D performance. In this work, a fault diagnosis based on a convolutional neural network (CNN) for 3D printers is proposed. We have leveraged an online repository of a set of data streams collected from working 3D printers. The CNN was used to process, detect and classify anomalies in 3D printing with appreciable accuracy. The proposed CNN outperformed the support vector machine (SVM), and artificial neural network (ANN) by 5.1% and 25.7%, respectively.