Open AccessNondestructive Detection of Ceramic Products Based on Tapping Sound Signal Feature Recogition
Author(s) -
Liping Liu,
Liucheng Jiang,
Lele Qiao
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2132/1/012026
Subject(s) - tapping , signal (programming language) , support vector machine , computer science , ceramic , finger tapping , signal processing , feature (linguistics) , process (computing) , pattern recognition (psychology) , artificial intelligence , nondestructive testing , sample (material) , engineering , materials science , digital signal processing , computer hardware , mechanical engineering , audiology , composite material , medicine , linguistics , philosophy , chemistry , chromatography , programming language , operating system , radiology
Recent studies on the test of ceramic non-destructive testing are mainly based on high cost technologies, image processing and so on, these method possesses some drawback of low efficiency, high cost and so on. What’s more, detecting whether the ceramic products by human through listening to sound of tapping is also effectless. This paper proposed a non-destructive method for ceramic products to solve this problem. This non-destructive method consists of a tapping device and a signal processing module. The tapping device will be applied to generate the tapping sound signal and the signal processing system will be applied to analysis signal. After the process of signal analysis, sample length and peak of spectrum 2 parameters is extracted, then use these parameters to train SVM, the results will be compared with BP neural network (BPNN). The result of experiment shows that SVM with different kernels of linear, poly, rbf, sigmoid respectively reach the accuracy of 96.29%, 96.29%, 46.29%, 93.82%, while BPNN reaches the accuracy of 93.21%. This result proves that SVM can effectively complete the task of identifying defective ceramics, and its performance is better than BPNN.