Statistical Process Control with Computer Vision

Roboflow Workflows can automate statistical process control data collection by using an object-detection model to inspect production images, extract a continuous feature such as welding-line bounding-box width in pixels, count visible defects, and export timestamped results with inspection status to a CSV file. The example uses a welding dataset with Workpiece, Welding Line, and Defect classes, trains an RF-DETR model, and applies workflow logic that records failed detections separately rather than treating them as valid zero measurements. The resulting data can be analyzed with pyControlCharts to create an XmR chart for dimensional variation and a c-chart for defects per unit, allowing teams to identify unusual shifts, trends, or spikes in process variation. Reliable deployment requires representative model validation, repeatable camera conditions, calibration when physical measurements are needed, accurate part and capture-time identifiers, and control limits established from a stable production baseline; c-charts also assume equivalent inspection opportunities, while varying areas or quantities may require a u-chart.

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