Manual or traditional machine vision inspection approaches make it hard for enterprises to scale up their operations: it is very resource intensive, or very costly due to the need for highly specialized labor.
Knowledge transfer takes time.
Low process consistency.
Not being able to link the results from your Visual AI stage to other metrics in the production line prevents you from achieving additional benefits in terms of automated root cause analysis of manufacturing problems and preventive maintenance.
Unexpected downtime of your manufacturing lines.
Lack of consistent failure root cause analysis.
Poor quality control model.
90%
Less manual labour
95%
Detection Accuracy
100%
Consistency
< 6"
Prediction latency
UI-based; easy to create AI models for different parts.
Detection & classification capabilities leveraging state-of-the art Convolutional Neural Networks (CNNs).
Creation of whole-part or specific component models, to enable further investigation.
Active Learning capabilities to facilitate labeling work.
Centralized solution, works with any type of camera.
Real-time prediction of parts (under 6s).
Keeps track of each part prediction for analysis and retraining.
Increases inspection accuracy and prevents stopping of production lines.
Individual and aggregated statistics at different levels (production line, factory, country, company).
Drill-down capabilities on each manufacturing facility and production line.
Possibility to add other metrics to monitor performance and perform Root Cause Analysis of problems.
It lays the foundations for implementation/integration with a real-time MES (Manufacturing Execution System).
Get started and request a demo to learn how AI Quality Control Toolkit can help you.
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