Transmission Condition Monitoring of 3D Printers Based on the Echo State Network

Applied Sciences

Diego Cabrera¹  Yun Bai²  Kun He²  Chuan Li²  Jianyu Long²  Shaohui Zhang² 

[1] Grupo de Investigación y Desarrollo en Tecnologías Industriales (GIDTEC), Universidad Politécnica Salesiana, Cuenca 010169, Ecuador;School of Mechanical Engineering, Dongguan University of Technology, Dongguan 523808, China;

关键词: echo state networks;    condition monitoring;    3D printer;    machine learning;    feature extraction;   

DOI  :  10.3390/app9153058

来源: DOAJ

【 摘 要 】

Three-dimensional printing quality is critically affected by the transmission condition of 3D printers. A low-cost technique based on the echo state network (ESN) is proposed for transmission condition monitoring of 3D printers. A low-cost attitude sensor installed on a 3D printer was first employed to collect transmission condition monitoring data. To solve the high-dimensional problem of attitude data, feature extraction approaches were subsequently performed. Based on the extracted features, the ESN was finally employed to monitor transmission faults of the 3D printer. Experimental results showed that the fault recognition accuracy of the 3D printer was obtained at 97.17% using the proposed approach. In addition, support vector machine (SVM), locality preserving projection support vector machine (LPPSVM), and principal component analysis support vector machine (PCASVM) were also used for comparison. The contrast results showed that the recognition accuracies of our method were higher and more stable than that of SVM, LPPSVM, and PCASVM when collecting raw data via the low-cost attitude sensor.

【 授权许可】

Unknown