Transmission condition monitoring of 3d printers based on the echo state network

Shaohui Zhang, Kun He, Diego Cabrera, Chuan Li, Yun Bai, Jianyu Long

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

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.

Original languageEnglish
Article number3058
Pages (from-to)3058
JournalApplied Sciences (Switzerland)
Volume9
Issue number15
DOIs
StatePublished - 29 Jul 2019

Keywords

  • 3D printer
  • Condition monitoring
  • Echo state networks
  • Feature extraction
  • Machine learning

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