Preliminary Study on Fault Diagnosis and Intelligent Learning of Fused Deposition Modeling (FDM) 3D Printer

Jiawei Liao,Gang Xiong,Chang Liu,Can Luo,Zhen Shen,Jian Lu

doi:10.1109/iciea.2019.8834376

Preliminary Study on Fault Diagnosis and Intelligent Learning of Fused Deposition Modeling (FDM) 3D Printer

Jiawei Liao, Gang Xiong + Show 4 more

https://doi.org/10.1109/iciea.2019.8834376

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Publication Date: Jun 1, 2019

Citations: 16

Affiliation: Beijing University of Chemical Technology, Institute of Automation, Chinese Academy of Sciences, Chengdu University, Sichuan University, Cloud Computing Center

#Fused Deposition Modeling #New Method For Fault Diagnosis + Show 8 more

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Abstract

With the development of intelligent manufacturing, 3D printing has been applied to more and more fields of industries. The Fused Deposition Modeling (FDM) is widely applied for 3D printing as a relatively matured 3D printing technology. There still exist some problems in failure rate, stability, nozzle spitting with FDM type of 3D printer, because this type of printing equipment lacks early warning systems. In this paper, we analyze the fault of FDM type 3D printer through monitoring of machine vibration signals as well as fault diagnosis of FDM 3D Printer based on sensors. By using these approaches, we reduce the dimension of the signature signal and compared it with the fault matrix. In summary, we propose a new method for fault diagnosis of FDM type 3D printer based on intelligent learning.

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