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

被引：0

作者：

Liao, Jiawei ^{[1
]}

Shen, Zhen ^{[2
]}

Xiong, Gang ^{[3
]}

Liu, Chang ^{[4
]}

Luo, Can ^{[3
]}

Lu, Jian ^{[5
]}

机构：

[1] Beijing Univ Chem Technol, Coll Mech & Elect Engn, Beijing, Peoples R China

[2] Chinese Acad Sci, Cloud Comp Ctr, Dongguan, Peoples R China

[3] Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing, Peoples R China

[4] Sichuan Univ, Coll Comp Sci, Chengdu, Peoples R China

[5] Guangdong Launca Med Device Technol Co Ltd, Dongguan, Peoples R China

来源：

PROCEEDINGS OF THE 2019 14TH IEEE CONFERENCE ON INDUSTRIAL ELECTRONICS AND APPLICATIONS (ICIEA 2019) | 2019年

基金：

中国国家自然科学基金;

关键词：

3D Printer; Fused Deposition Modeling; Sensor; Fault Diagnosis; Intelligent Learning;

D O I：

10.1109/iciea.2019.8834376

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

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. fly 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.

引用

页码：2098 / 2102

页数：5

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