The Tools You Need to Improve Defect Detection and Reduce Cost of Quality
Machine vision systems were supposed to automate defect detection – but instead often struggle in challenging situations.
And complicated, custom, high-value articles are often assembled incorrectly, which creates high labor and rework costs. If you suffer from those or related problems, Mariner has the Deep Learning AI tools you need to solve them. Learn more about our award-winning solutions:
Our Defect Detection and Assembly Verification Tools Solve Manufacturing Problems
Total Cost of Quality involves many factors, including labor costs, escapes, inspection costs, and scrapping and reworking costs, among others. Reducing any of them reduces your total cost of quality; attacking many of them at once, as our Spyglass Visual Inspection (SVI) and Spyglass Assembly Verification (SAV) solutions do, allows you to dramatically lower your total cost of quality and achieve a very fast return on investment (ROI).
How Deep Learning AI Reduces Total Cost of Quality
For decades, manufacturers have used traditional machine vision systems for product inspection and defect detection. In some cases, these machine vision systems work quite well. But for fuzzy problems, like telling lint from a stain on a roll of fabric, traditional machine vision systems often perform poorly and incorrectly catch a high number of pseudo-defects. These pseudo-defects, or false rejects, lead to the need for human reinspection and the high costs associated with that need. In contrast to traditional machine vision systems, our Deep Learning AI excels at correctly solving “fuzzy” problems, eliminating pseudo-defects and the need for reinspection. If you can see a defect in a product image and correctly identify it, so can our Deep Learning AI – and we’ll prove it to you at no cost.