Abstract
In order to improve the detection accuracy and efficiency of silicon nitride ceramic ball surface defects, a defect detection algorithm based on SWT and nonlinear enhancement is proposed. In view of the small surface defect area and low contrast of the silicon nitride ceramic ball, a machine vision-based nondestructive inspection system for surface images is constructed. Sobel operation is used to eliminate the nonuniform background, and the silicon nitride ceramic ball surface image is decomposed by SWT. And frequency-domain index low-pass filtering is used to modify the decomposition coefficients, and an adaptive nonlinear model is proposed to enhance defects; finally, the image is reconstructed and segmented by the stationary wavelet inverse transform and the dynamic threshold method, respectively. The enhanced algorithm can effectively identify surface defects and is superior to traditional defect detection algorithms.
Highlights
Si3N4 ceramic balls have many excellent properties, for example, high hardness, low coefficient of thermal expansion, and good self-lubrication, and under high temperature, high speed, and other harsh conditions, they can still maintain good strength and hardness [1,2,3]
In order to ensure the safety and reliability of the device, it is very important to detect the surface defects of silicon nitride ceramic bearing balls. e defect detection of Si3N4 ceramic balls mainly relies on manual detection, which has the disadvantages of high cost, low accuracy, and large randomness [8,9,10]. us, efficient machine vision detection methods are becoming more and more popular
Zhang et al [13], by analyzing the Si3N4 ceramic balls’ defect characteristics, found that some defects cannot be directly detected. They proposed a method based on fringe reflection
Summary
Si3N4 ceramic balls have many excellent properties, for example, high hardness, low coefficient of thermal expansion, and good self-lubrication, and under high temperature, high speed, and other harsh conditions, they can still maintain good strength and hardness [1,2,3]. Zhang et al [13], by analyzing the Si3N4 ceramic balls’ defect characteristics, found that some defects cannot be directly detected They proposed a method based on fringe reflection. Yang et al [14] developed an automatic inspection system based on NDT to detect Si3N4 balls’ defects In their method, a microscope and CCD camera were used to shoot various surface defects, and the top-hat transformation and logarithmic transformation were used to remove the uneven light interference. A microscope and CCD camera were used to shoot various surface defects, and the top-hat transformation and logarithmic transformation were used to remove the uneven light interference They proposed the defect automatic seed region growth algorithm to locate defects. (1) e proposed method can effectively detect various types of defects on the surface of Si3N4 ceramic balls, including pits, snowflakes, , scratches, and cracks, and can accurately locate the defect location (2) According to the image information, the parameters can be adjusted adaptively to realize the adaptive linear enhancement of defects (3) e proposed method can effectively detect lowcontrast defects and can be applied to defect detection in other fields
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