AI Visual Inspection – RAPID

In the world of precision manufacturing, failures in Visual inspection have real world consequences, and can mean high-profile recalls, dissatisfied customers, and damage to a hard-earned reputation. The conventional approach of Quality Control, manual reviews by highly trained and qualified inspectors, often faces challenges of product inconsistency, the high cost of inspector turnover, the required trainings, human unreliability, speed issues, and, ultimately, customer confidence issues. 

Using “machine vision” systems and automated imaging-based analysis does offer manufacturers a certain level of assurance in defect inspection. However, this safeguard can be expensive, complex and time-consuming to configure; the system may exhibit an inability to tolerate deviations that are actually acceptable, and when some anomalies are too difficult to catch, inconsistencies can occur, opening the door to a quality failure.
Researchers have long sought to develop reliable, repeatable, fully optimized Machine Learning that is adaptable, with a simplified set-up and interface that will dependably manage the most difficult of applications.

AI-based surface inspection algorithm called "One Class", which is a new feature of RAPID machine learning that is extremely powerful for manufacturing inspections. Image analysis requires a huge number of pictures of both good and defective products to be collected for learning by AI, but collecting pictures of defective products is no easy matter in the precision manufacturing industry. The "One Class" uses proprietary NEC technology to enable defective products to be identified just by learning pictures of good products. This property enables you to deploy AI rapidly to your production environment by yourself.

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