For a long time, the tissue paper industry has been plagued by numerous quality control pain points. Due to the complex production process and limitations of traditional machine vision technology, manufacturers mostly rely on manual visual inspection of finished products, which easily leads to missed detection of defective products. This not only increases labor costs but also causes a great waste of raw materials. At the same time, appearance issues such as bag bursting, paper damage, and missing date codes have emerged in the market, seriously affecting consumers’ first impression of the brand.
FPGA system inspection interface
Inspection image
The system installed on-site
Visual inspection machines focus on the entire process of the clean post-processing workshop. Starting from the base paper inspection in the folder section, they use FPGA technology, 8K cameras, and other tools to accurately identify defects such as stains and holes. Then, before the paper is put into bags, they inspect the paper’s size and stains to avoid packaging machine failures. Suivant, they conduct appearance inspection on small and medium packages, relying on AI deep learning algorithms to detect problems like bag bursting and coding errors. Finally, they check for missing packages and tape adhesion through carton packing inspection, forming a full-chain quality control system.
Packaging defect detection
This approach not only reduces batch quality problems from the source and lowers material consumption but also replaces manual inspection, significantly saving labor costs. The single-camera dual-strobe photography technology also reduces equipment investment. Meanwhile, it prevents defective products from entering the market, reduces consumer complaints, and enhances brand reputation.
