Application of machine vision in non-woven inspection
The demand for household paper such as diapers and sanitary napkins is constantly increasing, and the market's requirements for production capacity and quality of non-woven fabrics are constantly improving. Since non-woven fabrics are the main substrates for diapers and diapers, their surface quality will directly affect the quality of finished products and the safety of users in the later period. Due to the current production environment and production process of domestic non-woven fabrics, it is easy to cause defects such as mosquitoes, black spots, metal iron chips, stains, pulp blocks, impurities and other defects on the surface of the material during the production process. and other problems, the traditional manual visual inspection cannot meet the quality standards of hygiene products.
The non-woven stain detector can perform high-speed, high-precision online detection of surface defects for 7*24 hours on the non-woven production line running at high speed. Impurities, foreign bodies, stains, pulp lumps and other defects are automatically detected and alarmed, which not only improves product quality, but also saves labor costs and improves the competitiveness of enterprise products. The non-woven stain detector has been recognized by more and more non-woven enterprises and has become an indispensable part of high-speed and high-quality production.
Detection principle
In view of the material properties of non-woven fabrics with uneven thickness, sparse and uneven structure, and the randomness of defect distribution (front, back and middle of non-woven fabrics), The traditional transmission lighting method is easy to cause missed detection and false alarm of the system. The non-woven stain detector uses a shadowless bright field to form a detection environment, that is, one light source transmits below the material, and the other light source reflects above the material. When the production line starts to run, the CCD camera synchronizes according to the speed signal collected by the encoder. Scanning and shooting, the collected images are dynamically segmented through the image analysis software algorithm, and the grayscale difference between the defective image and the normal product is used to find the defect and perform operations such as alarm, statistics, classification, and recording.
Technical parameters
Non-woven defect pictures
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