Pharmaceutical companies have always benefited from the automation process. From testing to manufacturing, deep learning and vision inspection systems have helped these companies provide many life-saving medications and medical devices. More recently, these companies have been successfully employing the use of visual inspection and machine learning, which in other words, is artificial intelligence.
The USP (United States Pharmacopeia) Convention necessitates that all products associated with parenteral administration be visually inspected. There are strict requirements that pharmaceutical products be free of observable particulate matters. For instance, a container with a closure defect must be found and ejected. Inspecting all parts/products is labor-intensive and costly, especially when meeting high-volume production demands. Automated vision inspection systems have been a meaningful change for the pharmaceutical industry because, when implemented correctly, these systems speed up production and increase detection rates of faulty products/parts. Vision inspection operates quicker and for more extended periods than a human, and they also have a higher detection rate.
When vision inspection systems are implemented, systems run the risk of falsely identifying defects within a safe product. AI (artificial intelligence) coupled with ML (machine learning) can identify and fix issues associated with improper detection from vision systems. AI and ML learn about ejected project to identify the root cause of the ejection. Systems and engineers can take corrective actions and focus on actual defects with this information.
Implementing the latest technology into automated assembly systems dramatically speeds up production and creates a more consistent product. It would help if you did not hesitate to give our team a call for more information on designing and building automated assembly systems with vision inspection and machine learning.
