AI-driven quality testing can increase productivity by up to 50% and defect detection rates by up to 90% compared to human inspection. Though machines with automated optical inspection (AOI), powered by machine vision, have replaced most of the manual processes in the modern assembly line, quality control remains a huge and costly challenge.
AI software can be combined with a machine vision platform, which supports various deep learning applications to build a smart, automated optical inspection solution. In addition, an appropriately-optimized toolkit can enable deep learning inference from edge to cloud.
With AI, machine learning, and deep learning residing at the edge (or near where the computing process takes place), data can be recorded to analyze issues during the production process as the reference to adjust the manufacturing parameters to improve the yield rate and increase the overall equipment effectiveness (OEE). The very autonomous learning nature of deep learning allows one to conduct predictive analyses and reach error-free, flawless production.
By combining two subsets of AI (machine learning and deep learning) and strategically applying them to optical inspection, the companies successfully helped the contact lens manufacturer increase the inspection accuracy by 65% (compared to traditional machine vision inspection) and the throughput by 50x (compared to a human inspector).
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