Neurala VIA for Machine Builders

Improving OEE with AI

Measuring and improving overall equipment effectiveness (OEE) has become a best practice for manufacturers as they seek to deliver products with zero defects, as fast as possible, and with zero downtime. To this end, machine builders have begun to embrace sensing technologies, including cameras, that can accumulate data on key machine operating metrics and to provide a visual record of product quality or process adherence.

Partner Example: I.M.A. E-CO FLEX1

In this video, Neurala VIA automates the inspection of package seals to verify their integrity prior to shipping. What makes the challenge uniquely suited for Neurala VIA is the variability in taping locations and also the frequency with which the tape's appearance may change - requiring quick model retraining to ensure optimal ongoing performance.

Use Case Example: Sensor Data Monitoring

Neurala's ability to train AI model's using just 'good' samples extends beyond visual applications. In fact, Neurala's anomaly capability can learn the normal operating mode from a wide variety of sensors in a wide variety of operating environments and temperatures. This means that, upon commissioning and during early usage, a machine's regular operating cycle across a family of sensors in combination can be learned and monitored. With this baseline established, any deviation can be flagged to an operator before a material negative impact to production occurs, permitting the operator to schedule routine maintenance before a failure takes down the line during production.

Why add AI to your Machine?

    • Provides opportunities for increased throughput by automating manual inspection process performed by your clients' personnel
    • Increases customer margins by reducing inspection costs and minimizing product waste by more quickly identifying defects
    • 'Always on' process monitoring can identify deviations from normal operations to highlight anomalous sensor data before it manifests in product defects, providing opportunities for lower - cost scheduled versus expensive unplanned maintenance

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