Vision AI QC for In-Run
Defect Detection
Use Case
Detect Surface and Dimensional Issues in Real Time During Production
Business Challenge
Traditional inspection processes often identify quality problems too late, leaving manufacturers exposed to waste, delays, and lost customer confidence. Without real-time visibility, early defects escape detection until final checks.
- After-the-fact inspections miss surface or dimensional defects that appear during machining, causing costly rework and schedule disruptions.
- Scrap accumulates across production runs before problems are noticed, increasing material losses and inflating operational costs.
- Manual quality checks vary by operator, leading to inconsistent pass/fail criteria and unreliable records for certification.
- Late discovery of defects delays shipments, impacting customer satisfaction and risking contractual penalties.
The AI Approach
A Vision AI-driven QC system was deployed to capture issues in-run, improve consistency, integrate seamlessly with manufacturing workflows, and provide continuous, real-time visibility into part quality.
- In-run cameras applied trained thresholds to detect variations caused by changing parts, tool wear, and variable lighting conditions.
- Feature Measurement & Anomaly Flags provided objective pass/fail decisions, standardizing criteria across operators and product families.
- Edge Processing enabled local decision-making to prevent delays, with results automatically synced to MES and ERP systems.
- Continuous learning updated thresholds over time, adapting detection accuracy as new part designs and revisions were introduced.
Project Deployment Overview
Input Data Used
Labeled images, CAD definitions, feature libraries, part IDs, and revision metadata formed the training baseline.
Final Output Generated
Automated pass/fail decisions, timestamped image logs, and QC summaries created reliable quality records.
Deployment Platform
Vision AI module deployed at the edge, with REST connectors for integration into MES and ERP systems.
Processing Scope
Configured for single or multi-camera cells, variable lighting, rotating parts, and pose-based inspection templates.
Business Outcomes & Value Unlocked
The AI-driven QC platform reduced waste, accelerated feedback loops, and provided repeatable records for compliance and certification, while also lowering costs, improving reliability, strengthening traceability, and enhancing overall manufacturing performance.
Lower Process Scrap Rates
Scrap decreased 20-35% across targeted product families, saving material and machining costs.
Faster Quality Feedback
Inline defect detection shortened critical feedback loops from days to just minutes on the shop floor.
Repeatable Inspection Standards
Pass/fail decisions became fully consistent across operators, shifts, and varied product variations.
Simplified Audit Readiness
Timestamped logs and QC reports streamlined certification and compliance audits.