基于时频分析和深度学习的表面粗糙度超声模式识别方法

doi:10.19595/j.cnki.1000-6753.tces.210707

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Abstract Surface roughness directly determines the performance and service life of the workpiece. As customary surface roughness detection methods based on optical or three-dimensional profilometers have higher requirements on the surface cleaning state and operating environment, a non-contact electromagnetic ultrasonic surface roughness recognition method based on deep learning is proposed under this paper. Firstly, the effects of eddy current density and the Lorentz force on the excitation and reception signals are investigated by establishing the finite element simulation model of electromagnetic ultrasound with different surface roughness. Then, the proposed convolutional neural network is utilized to extract the features of the time-frequency coefficient map of the A-scan signal detected by the electromagnetic ultrasonic transducer, which is input into the pre-trained support vector machine classifier to complete the roughness recognition and prediction. To verify the proposed method, the surface roughness comparison block processed by the end milling process is tested. The experimental results show that the average accuracy of the proposed ultrasonic recognition method is 98.83%, which has high prediction accuracy and stability, solves the problem of the low signal-to-noise ratio of the ultrasonic signal which leads to difficult signal feature recognition, and reduces the dependence of feature extraction process on manual intervention.

Key words： Surface roughness electromagnetic acoustic transducer（EMAT） deep learning convolution neural network image recognition

Received: 15 May 2021

PACS:	TB553
	TP183

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	Articles by authors
	Cai Zhichao
	Sun Yihu
	Zhao Zhenyong
	Li Yibo

Cite this article:

Cai Zhichao,Sun Yihu,Zhao Zhenyong等. A Deep Learning-Based Electromagnetic Ultrasonic Recognition Method for Surface Roughness of Workpeice[J]. Transactions of China Electrotechnical Society, 2022, 37(15): 3743-3752.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.210707 OR https://dgjsxb.ces-transaction.com/EN/Y2022/V37/I15/3743

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