Drug delivery device final assembly verification

Ensure device completeness and functionality after packaging

Two assembly problems are discovered in the x-ray image of a drug delivery device by using deep learning

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During the process of final assembly and packaging, drug delivery devices such as autoinjectors, pens, cartridge-based systems, and prefilled syringes can be dislocated or misaligned, even if subassemblies have been previously inspected and passed. Such damage inside sealed packaging has previously been hard to detect.

These devices are often used in emergencies, and undetected damage can cause malfunction or harm to patients. Nonfunctional packaged devices mean that there may be less available inventory than planned.

X-ray imaging of final packaging can provide an image of the assembled device, but the complexity of the image and the variety of possible defects make it difficult.

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Cognex Deep Learning is ideal for X-ray inspection and verification of assembled and packaged devices. The assembly verification tool trains on a set of images of correctly assembled devices with undamaged components and learns the full range of acceptable variation in location and position of the various parts. Once trained, it quickly identifies and rejects those assemblies that have bent, incorrectly positioned, or missing parts as well as incorrect volumes of medication, while accepting the full range of properly assembled devices.

End users can have confidence that drug delivery devices will be free of defects caused by final assembly and packaging and be ready for emergency use.


Two assembly problems are discovered in the x-ray image of a drug delivery device by using deep learning


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