Machine vision-based transverse vibration measurement of diamond wire

Diamond wire sawing is one of the key technologies for wafering in the semiconductor industry. The transverse vibration of the wire during sawing will generate additional displacement that not only reduces the as-cut wafer quality but increases the critical adhesion gap between the wires, which is an important factor limiting the development of diamond wire slicing technology toward narrower kerf loss and thinner as-cut wafer. The vibration measurement of the diamond wire provides an indispensable test basis for its vibration control. Therefore, a machine vision-based transverse vibration measurement method of diamond wire is developed in this paper. The approximate edge position of the wire in the image midline is obtained to crop the region of interest in the image by using the maximum continuous same-directional pixel gradient algorithm and thus reduce the computing time. The wire edges in the image are accurately extracted by an adaptive image segmentation method proposed based on the image histogram. According to the surface topography characteristics of diamond wire, a feature extraction algorithm based on the multiple inward line fitting using the least square method is proposed, which can effectively detect the position of the inner diameter boundary of the diamond wire. Accordingly, the equation of the central axis of the wire in the image is obtained to realize the transverse vibration detection of the diamond wire. To conclude, a diamond wire vibration measurement experiment is conducted and the machine vision detection results are compared with the vibration displacement measured by the accelerometer at the same position of the wire end. The results show that the measurement error of the machine vision vibration inspection is generally small for the different types of diamond wires and the different frequencies of the wire transverse vibration, which means that the measurement method proposed in this paper has good accuracy, and applicability, and robustness.