Print defects take endless forms, and enumerating defect samples is nearly impossible. DaoAI APDT flips the approach—learn only good samples and treat any deviation as suspect—so it can go live fast even when samples are scarce.
A home-appliance plant prints brand logos, model silkscreens and safety marks on panels and housings. One line serves multiple brands and regional variants, so plates and text content change often. Print defects are highly divergent: double-print, broken-print, missing strokes, ink smearing, position drift, uneven density—many only a few tenths of a millimeter, easily missed by human eyes under fast takt.
Supervised methods demand ample samples for every defect class, but print defects are sporadic and infinitely varied—impossible to enumerate; and every plate change means rebuilding samples and models, making go-live slow and maintenance heavy. The plant needed an approach insensitive to sample count that could restore inspection capability quickly after each changeover.
DaoAI solution: APDT positive-sample learning defines the standard from 1–20 good samples
DaoAI uses APDT positive-sample learning, establishing a baseline from just 1–20 well-printed good samples; the system automatically learns the normal form of character strokes, logo contours and ink distribution. Any double-print, broken-print or missing stroke that deviates from the good sample is flagged as a defect—no defect samples required in advance.
- APDT goes live on just 1–20 good samples, no defect samples needed, rebuilding the baseline fast after a plate change
- Reliably catches double-print, broken-print, missing strokes, ink smearing and position drift
- Inspection throughput rises over 40% versus manual, with in takt full inspection no longer reliant on human eyes
- Changeover is zero-code, so line operators can switch baselines themselves
No need to predict what a defect looks like—if it doesn't look like a good sample, it gets stopped.
After go-live the printing station achieves full inspection within takt, and high-frequency defects like double-print and missing strokes are reliably caught before shipment. Changeover shifted from engineer-dependent tuning to fast on site switching, inspection throughput rose over 40% versus the prior manual process, and brand print consistency improved markedly.
FAQ
How does DaoAI APDT solve the problem of scarce samples for printing defects?
DaoAI APDT uses positive sample learning. Only 1-20 good printed products are needed to establish a detection benchmark. Any deviation from the good products is regarded as suspicious, eliminating the need to exhaustively list defect samples and enabling rapid launch even with scarce samples.
What are the advantages of APDT compared with traditional supervised methods?
Traditional methods require collecting sufficient samples for each type of defect, and need to redo samples and models when changing versions, resulting in slow launch and heavy maintenance. APDT is insensitive to samples, allows 0-code operation when changing versions, can quickly restore detection ability, and improves efficiency by over 40%.
What printing defects can APDT detect?
APDT can stably identify printing defects such as double-printing, broken-printing, missing strokes, ink trailing, position offset, and uneven color. It enables full inspection within the production cycle at the printing station and effectively intercepts high-frequency defects.