In fiber optic cable production, the detection and removal of minute internal defects is crucial for ensuring communication quality. These defects may include microcracks, bubbles, impurities, or fiber core inhomogeneity. Failure to detect them promptly can lead to signal attenuation, transmission interruption, or even system failure. Therefore, high-precision detection technologies and intelligent methods are essential for accurate defect identification and efficient removal.
Traditional manual inspection relies on microscopic observation, which is inefficient and susceptible to subjective factors, making it unsuitable for large-scale production. In modern fiber optic cable production, machine vision technology has become a core inspection method. Based on high-precision industrial cameras and customized optical systems, high-speed scanning of the fiber surface and interior can capture micron-level defects. For example, using a high-speed line scan camera and a low-distortion lens, combined with a high-brightness, uniform light source, ensures that the pixel accuracy of image acquisition reaches the micron level, thereby guaranteeing defect resolution at the optical level. Multi-angle imaging technologies, such as multi-camera arrays or rotating inspection modules, can achieve 360-degree coverage of the fiber without blind spots, avoiding missed detections due to visual blind zones.
Intelligent image processing algorithms are key to improving inspection accuracy. Traditional algorithms extract defect features through edge detection and threshold segmentation, but their adaptability to complex defects is limited. The introduction of deep learning algorithms enables the system to autonomously learn the morphology and texture of defects, significantly improving the recognition rate. For example, convolutional neural networks (CNNs) can classify fiber optic images, distinguishing between normal and defective areas; while object detection algorithms (such as the YOLO series) can accurately locate defects and label their type and severity. By training on massive amounts of defect samples, the model can adapt to defect morphologies in different production scenarios, achieving highly robust detection.
The design of automated inspection processes must balance efficiency and accuracy. Fiber optic cable production is typically high-speed and continuous, requiring seamless integration of the inspection system with the production line for real-time detection and feedback. For example, an edge-based real-time inspection architecture can be adopted, using an embedded vision analysis module to quickly process acquired images, immediately triggering alarms upon defect identification and recording defect location information. Simultaneously, the system can link with PLC or MES systems to automatically adjust production parameters or mark defective products, preventing unqualified products from flowing into the next process. For complex defects, the cloud-based intelligent re-inspection platform can perform secondary analysis based on a large model, providing more accurate defect classification and process optimization suggestions.
The defect rejection process requires the integration of mechanical control and quality traceability technologies. Once a defect is detected, the system must quickly locate its position and reject it using methods such as robotic arms, laser cutting, or inkjet marking. For example, during fiber optic drawing, if an abnormal core diameter is detected, the drawing speed or temperature parameters can be adjusted immediately; if surface scratches are found, the defect location is marked using inkjet marking equipment for subsequent sorting. Simultaneously, the system needs to record information such as the production batch, defect type, and location of defective products, forming a quality traceability database to provide data support for process improvement.
The integration of multi-dimensional detection technologies can further improve the defect detection rate. For example, combining optical time domain reflectometer (OTDR) and machine vision can simultaneously detect the transmission performance and surface defects of optical fibers; using X-ray or ultrasonic detection technologies can reveal the internal structure of optical fibers and identify deep defects. Furthermore, distributed optical fiber sensing technology can monitor parameters such as stress and temperature of optical fibers in real time, providing early warnings of potential defect risks and enabling preventative quality control.
Defect detection in fiber optic cable production requires a closed-loop system of "detection-analysis-rejection-feedback". By integrating high-precision machine vision, intelligent algorithms, automated control, and multiple technologies, precise identification and efficient removal of even minute defects can be achieved, thereby improving the transmission performance and reliability of fiber optic cables and providing a solid guarantee for the high-quality operation of communication networks.
