Blister lnspection Machine

Type of Defect Detected

Missing grain, half grain, unaligned aluminum foil and plastic plate, batch number punch through, missing batch number, batch number cut, hair in plate, tablet broken, black spots/foreign objects on grain, double cap, aluminum foil shavings on plate, missing aluminum foil, powder on plate, stain on plate, grain concave drug loss, mesh pleats, finesse defect, unclear mesh, squashed blister, light-colored foreign body, drug grain convex foreign body, batch number offset, plate surface drug powder inclusions, plate in wrong dimension, and so on.

Technical Parameters

Product Features

• Deep learning algorithms detect hard-to-identify defects

For defects that are difficult to detect by traditional algorithms, the deep learning algorithms are used. By providing the algorithms with a specified amount of defect data, the deep learning algorithms become increasingly accurate and are better able to detect inconspicuous defects. This is advantageous in detecting minor damage and defects, such as unclear mesh.

• Detect Aluminum foil broken

It can detect defects in the aluminum foil surface of the tablet, to avoid quality problems caused by the deterioration of pharmaceutical products due to aluminum foil damage. 

• Comfortable interactive experience

18.5-inch adjustable angle touch screen, intimate UI design to enhance the sense of human-computer interaction experience.