As and systems continue to develop for vision system applications, we will see more novel ways of adapting the solutions to replace traditional image processing techniques.
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Integrated quality inspection processes continue to make a significant contribution to medical device manufacturing production, including the provision of automated inspection capabilities as part of real-time quality control procedures. Long before COVID-19, medical device manufacturers were rapidly transforming their factory floors by leveraging technologies such as (), machine vision, , and .
These investments have enabled them to continue to produce critical and high-demand products during these current times, even ramping up production to help address the pandemic. Medical device manufacturers must be lean, with high-speeds, and an ability to switch product variants quickly and easily, all validated to ‘Good Automated Manufacturing Practice’ (GAMP). Most medical device production processes involve some degree of vision inspection, generally due to either validation requirements or speed constraints (a human operator will not keep up with the speed of production). Therefore, it is critical that these systems are robust, easy-to-understand and seamlessly integrate within the production control and factory information system.
Deep learning
Historically, such vision systems have used traditional machine vision algorithms to complete some everyday tasks: such as device measurement, surface inspection, label reading and component verification. Now, new “” algorithms are available to provide an ability for the vision system to “learn”, based on samples shown to the system – thus allowing the quality control process to mirror how an operator learns the process. So, these two systems differ: the traditional system being a descriptive analysis, and the new systems based on .
Innovative machine and processes ensure more robust recognition rates. Medical device manufacturers can benefit from enhanced levels of automation. Deep learning algorithms use classifiers, allowing image classification, object detection and segmentation at a higher speed. It also results in greater productivity, reliable identification, allocation, and handling of a broader range of objects such as blister packs, moulds and seals. By enhancing the quality and precision of deployed machine vision systems, this adds a welcome layer of reassurance for manufacturers operating within this in-demand space.
Deep learning has other uses in medical device manufacturing too. As relies on a variety of methods, including and
Deep learning can undoubtedly improve quality control in the medical device industry by providing consistent results across lines, shifts, and factories. It can reduce labour costs through high-speed automated inspection. It can help manufacturers avoid costly recalls and resolve product issues, ultimately protecting the health and safety of those towards the end of the chain.
However,
The main issue is the limited ability to validate such systems. As the vision inspection solution utilising
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