Application of a neural network integrated with the internet of things sensing technology for 3D printer fault diagnosis

  • Chih-Ta YenEmail author
  • Ping-Chi Chuang
Technical Paper


As a rapidly developing and maturing technology, 3D printing has substantially simplified the manufacturing of complex components. To improve the safety and practicability of machinery, fault diagnostic systems have become essential. This study employed a neural network algorithm to implement fault diagnosis. This study proposed a 3D printer fault diagnostic system that incorporated a neural network with a graphic interface. The MPU6050 accelerometer was combined with an Arduino microcontroller to monitor the status of a 3D printer. The neural network simulated the relationships of faults and training, and adopted different transfer functions to compare their training and convergence performance. The data were transferred to Laboratory Virtual Instrument Engineering Workbench after training and were displayed on a human machine interface, enabling users to explicitly identify the potential location of faults in a machine. This system ameliorates the work efficiency of managers and achieves real-time fault diagnosis. Moreover, the proposed 3D printer fault diagnostic system identified neural network parameters (e.g., the number of neurons, the learning rate, and the number of training) suitable for this system through various static and dynamic simulation experiments. Various shapes were adopted for testing the detection rate of the 3D printer fault diagnostic system. Finally, the real-time dynamic printing status data were recorded for approximately 10 min consecutively, yielding 6000 pieces of data, and that the transfer functions in the hidden and output layers were adopted for static simulation. The results showed that the overall fault detection rate of the system was as high as 83.5%.



This study was supported in part by the Ministry of Science and Technology MOST 106-2622-E-150-018-CC3 and National Formosa University 107AF06.


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© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Electrical EngineeringNational Formosa UniversityHuweiTaiwan

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