Inspection
Deep learning teaches your vision software how to distinguish seemingly defective products from actual rejects. This saves millions in costs.
False rejects are expensive. Good products that are ejected because of seeming defects cost the pharmaceutical industry millions every year. Vision systems that are configured with fixed threshold values often consider harmless bubbles or splashes on the side wall of lyophilized products to be defects. As a result, up to 15 percent of all containers are mistakenly declared to be rejects. Conducting a semi-automatic re-inspection for each automatic machine accounts for up to € 1 million per year. Investing in deep learning will reduce these reoccurring production costs and usually amortize in less than a year.
Let your machine learn so that you can avoid expensive false rejects
The software guides the user through the process of image data labelling in order to classify defects and train the algorithm, as well as the test of the AI precision. After the algorithm has passed digital validation, it can be integrated into the machine’s vision configuration. Combining the advantages of classic vision tools with the strengths of deep learning reduces complexity and ensures a faster cost reduction.
Find out more about how Eject-X can help you integrate deep learning into your inspection process, how the system learns — and how much money you can save.
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Reduced costs
Increased equipment availability
Improved quality control
