Quality and inspection
Catching the defect on the line, not in the return
Quality teams still inspect a sample of what comes off the line and investigate a defect's root cause well after it's already shipped. Yield prediction runs on last month's numbers, not this shift's, so a drifting process goes unnoticed until the returns start arriving.
AI inspects more of the line than a person can. Computer vision classifies defects from images as the product moves. Predictive models forecast yield and prioritize which lines need inspection first. Agents trace a defect back to its root cause on the process data, instead of a team doing it by hand. Fewer defects reach a customer, on infrastructure you control.
3 use cases
Factory-floor model deploymentBuild predictive models centrally and score them on plant infrastructure next to the line, including plants whose networks are segmented from the cloud.Predictive manufacturing designAI predictive manufacturing design learns from material properties, past configurations, and test results to predict which designs are likely to pass.Visual defect detection and inspection triageComputer vision models that classify defects in inspection images and rank lots by defect risk, so inspectors spend their time where defects are most likely.
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