Fiber optic detection equipment

With the increasingly strict requirements for fiber quality, more and more manufacturers are paying attention to the surface quality of the fiber itself.

Optical fiber defects, scratch detection, marks, broken edges, scratches, folds, broken edges, scratches, and other appearance defects are the main defects on the surface of optical fibers. Even small defects can affect the quality of products.

At present, traditional production-oriented enterprises rely on human eyes for defect detection. However, due to the high cost of manual inspection, easy fatigue, and the inability of human eyes to detect on high-intensity production lines, traditional human eye detection cannot meet the requirements of high-quality production processes.

Our fiber optic defect deep learning AI detection system can perform high-speed and accurate defect detection online, combined with on-site process online alarm, report statistics, and product grading processing, providing effective solutions for enterprises to ensure product quality.

The advantages of the machine are as follows:

1. Can detect defects in the appearance of optical fibers;

2. Can improve the accuracy and efficiency of detection;

3. The equipment operation is simple,

4. Low failure rate;

Performance parameters of the equipment:

1. Equipped with automatic sorting and labeling function for defective products;

2. Real time monitoring of product appearance quality can be achieved;

3. The detection accuracy can reach up to 0.01mm;

4. Capable of automatically identifying surface defect features ranging from 0.1mm to 1mm;

Equipment system functions:

1. Automatic correction function, automatically correcting tilted products to achieve stable detection;

2. Automatic statistics (good products, defective products, and total quantity);

3. The system has automatic learning function, and the learning process is easy to complete;

4. Design the tolerance range independently, and the system will determine whether the product is qualified based on the tolerance;

5. Are there any skewness, missing parts, or poor dimensions;

Detection effect image

Defects: scratches, damages, stains, impurities, missing materials, broken materials, etc