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Technical writing on AI inspection
Practical articles on defect detection, camera setup, model training, and MES integration from the Gaugegrove engineering team.
AI Cameras in Automotive QA: What Works, What Doesn't, and What You Need to Know Before Deploying
A candid assessment of where camera-based AI inspection reliably outperforms statistical sampling in automotive manufacturing and where it still needs human judgment in the loop.
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Reducing False Positives in Production Defect Detection Without Sacrificing Recall
Practical threshold tuning strategies for keeping false positive rates below 1% while maintaining defect recall above 98%.
Integrating Machine Vision with MES: Webhook Architecture for Real-Time Defect Traceability
How to structure defect event payloads and webhook delivery for reliable MES integration without middleware.
The True Cost of a Defect Reaching Final Assembly: A QA Engineer's Accounting
Breaking down rework, warranty, and recall costs to show why early-stage inspection ROI compounds faster than most plant engineers expect.
Defect Detection in Stamped Metal Parts: Camera Angle, Lighting, and Model Trade-offs
How directional lighting, camera position, and model architecture choices interact for surface crack detection on formed metal.
Lighting Setup for an AI Vision Model: Why Your Camera Choice Matters Less Than Your Light Source
A guide to raking light, dark-field, and structured illumination for surface defect detection in industrial environments.
Real-Time Inspection vs. Batch Review: When Latency Matters and When It Doesn't
Evaluating the quality and cost implications of inline detection versus end-of-shift batch review across different production scenarios.
Class Imbalance in Defect Datasets: Practical Techniques for Training on Real Production Data
Handling extreme class imbalance when defects are 1 in 500 parts and you can't wait to accumulate a balanced dataset before going live.
Pass/Fail Threshold Configuration: Setting Inspection Thresholds That Your QA Team Will Actually Trust
A structured approach to threshold selection using shadow mode data and business cost trade-offs instead of arbitrary confidence cutoffs.
Factory Cameras and Cloud Security: What Data Leaves Your Plant and How to Control It
What image data, inference results, and metadata actually leave the edge unit and how air-gap and on-premise options change that equation.
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