Identification and classification of bearing steel bars based on low-frequency eddy current detection method

Raw material testing is of great significance to guarantee the reliability of bearing ring production. Traditional testing methods such as spark identification are unable to run online, leading to the risk of raw material misuse in bearing ring production. To this end, this paper develops a low-frequency differential eddy current detection method for identifying and classifying raw materials of bearing steel bars. Firstly, the distribution of eddy current density inside bars and effects of excitation signals on the induced signals are discussed via numerical simulation. Then, the low-frequency eddy current detection experimental system with a phase-locked amplifier algorithm is established. Experiment results shows that the method developed in this study can distinguish between carbon steel and bearing steel reliably, identify the furnace number and heat treatment modes of bearing steel bars effectively, laying the foundation for online detection of raw material during bearing production.