Online detection method for magnetic suspension concentration based on machine vision

Abstract

With the intelligent development of magnetic particle inspection, the quality of magnetic indications formed at cracks is closely related to the accuracy of magnetic particle inspection image analysis results. The concentration of magnetic suspension is a key process parameter affecting the quality of magnetic indication formation. Hence, this study presents an online detection method based on machine vision for measuring magnetic suspension concentration. The method initially enhances the contrast of images of the pear-shaped measuring tube containing magnetic suspension and then extracts scale lines through feature analysis and morphological processing. A method for extracting the magnetic particle sedimentation area of magnetic suspension based on a dual-threshold segmentation algorithm is proposed. The contour filtering algorithm and pixel calibration method are used to obtain the magnetic particle concentration of the non-estimation and estimation areas based on scale line extraction, ultimately forming an online accurate detection method for magnetic suspension concentration values. Experiments were conducted to validate the method against different concentrations, turbidity levels, tilting angles of the pear-shaped measuring tube, and ambient brightness. The results show that the error in magnetic suspension concentration detection based on this method is within 5%. This has certain reference value for the stable control of magnetic suspension concentration and for enhancing the reliability of intelligent decision-making results in magnetic particle inspection.