Real-Time Batch Detection System for Defects in Highly Curved Spiral Cylindrical Coils

Tungsten coil is one of the important components of the magnetron, and its surface quality and geometry directly affect the service life of the magnetron. The focus of this article is to apply machine vision and deep learning algorithms to study a surface defect detection and geometric measurement system for tungsten coils. Firstly, in order to solve the problem of difficulty, high cost, and low efficiency in manual detection caused by small and random surface cracks on tungsten coils, this paper proposes a surface crack detection method based on deep learning technology. By constructing the dataset and training the semantic segmentation model, the surface cracks were effectively detected and identified. Secondly, a sub-pixel edge detection method based on image processing technology and the Canny algorithm is proposed to address the issue of difficulty in measuring the geometric dimensions of tungsten coils due to their high bending spiral structure. The method accurately measures the length, outer diameter, and the maximum error is only 0.023mm. The results of the verification experiment indicate that the developed system can detect an average of 70 tungsten coil samples in one minute, with sensitivity and accuracy of 99.66% and 98.52%, respectively. This system not only has high robustness and efficiency, but also reduces 95.23% of manual workload, meeting the requirements of production lines for surface defect detection and geometric dimension measurement of tungsten coils, and solving the limitations of traditional manual detection.