Classification of steel balls by resonant eddy-current sensor

Author:

Dao Duy-VinhORCID,Jeng Jen-TzongORCID,Doan Van-Dong,Dinh Chinh-HieuORCID,Pham Thi-TrangORCID,Nguyen Huu-Thang

Abstract

Abstract Objective: The quality and work-life of ball bearings depend on the material properties of the steel ball, hence it is necessary to carefully classify their properties for bearings and related applications. This paper presents the classification of steel balls based on the subtle differences in their electromagnetic properties. Approach: The conductivity and magnetic susceptibility for steel balls of the same kind are measured to investigate their correlation with eddy-current (EC) signals. Main results: The developed EC sensor works at the resonant frequency of 117 kHz with an optimal readout resistance of 15 kΩ, which helps to boost the signal level without a high-gain preamplifier. To detect the EC signal, the steel ball under test moves through the pickup coil, and the recorded data are used to build a voltage probability map (VPM) for the classification of the steel ball properties. Experimental results show that the steel balls with and without the hardening process can be identified by the change in the amplitude and phase of the EC signal, which is consistent with the observed change in the electromagnetic properties of steel balls. Significance: The built system can be applied to the related industries to check the quality of steel balls before use.

Funder

Ministry of Science and Technology, Taiwan

Publisher

IOP Publishing

Subject

Applied Mathematics,Instrumentation,Engineering (miscellaneous)

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Liftoff-Insensitive Conductivity Mapping Using a Self-Resonant Eddy-Current Probe;IEEE Transactions on Instrumentation and Measurement;2024

2. Bearing Ball Property Estimation using Multi-frequency Eddy-current Testing;2023 IEEE International Instrumentation and Measurement Technology Conference (I2MTC);2023-05-22

3. Strip steel surface defect detecting method combined with a multi-layer attention mechanism network;Measurement Science and Technology;2023-02-06

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