Tube yarn appearance defect inspection - Glass fiber product line - Product center

CN EN

400-900-8515

Fiberglass product line

Yarn tube appearance defect detection

The automatic appearance inspection system for fiberglass tubes breaks through the limitations of traditional machine vision inspection. It uses artificial intelligence deep learning to classify product defects in detail, which not only improves inspection speed and reduces labor costs, but also provides reliable reference data for automatic quality inspection systems, which is of great significance to enterprise quality control.
Currently, the detection accuracy rate has reached 99%, and the classification accuracy rate is 98%.

Applicable scenarios: Composite materials industry / Textile industry / Fiberglass industry

Demo video

Inquiry Hotline 400-8800-716

Product Features

Artificial intelligence technology

Automatic detection, automatic classification, automatic rating

Convenient automatic detection

Automatically locates and fixes yarn ends before inspection; automatically removes static electricity and dust; automatically weighs the yarn tubes; automatically separates qualified yarn tubes from defective yarn tubes.

Detecting multiple types of defects

Defects detected include: cylindrical surface of the yarn tube, slanted fuzz, loops, tufts, loose yarn, stains, soiled yarn, uneven bottom shape, obvious watermarks, wavy lines, and fuzz on the bottom of the yarn tube.

Record detection data

It can record test data for each yarn bobbin, and save images of stains and defects in the upper and lower yarn bobbins to a database, which can be stored for 3 months. Furthermore, yarns that have been manually inspected re-enter the inspection channel will not create duplicate records.

Output statistical reports

Statistical reports can be generated as required.

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