1. School of Mechanical Engineering, Jiangsu University of Science and Technology, Zhenjiang 212003, China 2. Jiangsu Key Laboratory of Advance Manufacturing of Ship and Ocean Machinery Equipment, Zhenjiang 212003, China
An acceleration vibration sensor was used to monitor the working state of the motor in the wire feeder, in order to study the effect of abnormal flow ratio (the ratio between the printing speed of the printer and the extrusion speed of the wire material) on nozzle blocking or lamination of printed products in the additive manufacturing process. The vibration signal of the wire feeder motor in different motion state during the printing process were collected, and the Fourier transform method was used to convert the time domain signal into frequency domain signal. Based on a frequency domain data, the characteristic value that characterizes the difference between each group of signals was extracted, KNN classification algorithm and K-fold cross-validation were introduced, the characteristic quantity was studied to clarity the relationship between the failure mode and the signal, to identify the different motion states of the wire feeder. Experimental results show that the proposed monitoring method has an accuracy of 92.73% for the identification of abnormal flow ratio, by using the signal frequency domain data difference as the characteristic quantity.
Xiao-wei LIU,Yun CHEN,Si ZHANG,Kang CHEN. Dynamic monitoring and identification of wire feeder in FDM-based additive manufacturing. Journal of ZheJiang University (Engineering Science), 2021, 55(3): 548-554.
Fig.2Monitoring platform of wire feeding mechanism
Fig.3KNN Classification algorithm schematic
Fig.4The diagram of K-fold cross-validation
真实值
预测值
正例
负例
正例
A
B
负例
C
D
Tab.1Confusion matrix of binary classification
真实类
预测类
1
2
$ \cdots $
n
1
${a_{11}}$
${a_{12}}$
$ \cdots $
${a_{1n}}$
2
${a_{21}}$
${a_{22}}$
$ \cdots $
${a_{2n}}$
$ \vdots $
$ \vdots $
$ \vdots $
$\vdots$
$ \vdots $
n
${a_{n1}}$
${a_{n2}}$
$ \cdots $
${a_{nn}}$
Tab.2Confusion matrix of multi-classification
Fig.5Time-domain diagram of vibration signals in different motion states of wire feeding mechanism
Fig.6Amplitude-frequency diagram of vibration signal in different motion states of wire feeding mechanism
Fig.7Accuracy of different Ҡ
Fig.8Average results of 10 times 5-fold training
Fig.9Classification results of different motion states signals of wire feeding mechanism
真实值
预测值
类1
类2
类3
类1
54
3
3
类2
2
95
3
类3
0
5
55
Tab.3Confusion matrix of tri-classification of different motion states of wire feeding mechanism
类别
SE/%
SP/%
AN/%
类1
90.00
98.75
92.73
类2
95.00
93.33
92.73
类3
91.76
96.25
92.73
Tab.4Numerical statistics of tri-classification
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