Visual Quality Inspection
Automatically detect defective parts using cameras.
Visual quality inspection detects surface defects, dimensional deviations and assembly errors using camera systems and computer vision. It complements or replaces manual visual checks at critical stations, boosting both accuracy and throughput.
- Consistent inspection quality regardless of human factors
- Higher throughput at inspection stations
- Complete documentation of every inspection
missed defects
inspection speed
payback period
inspection labor effort
Estimated from current escaped-defect rates and the cost of manual inspection stations.
- 01
Build defect catalog
Define relevant defect types together with QA.
- 02
Image data capture
Install camera systems at critical inspection points.
- 03
Model training
Train vision models on labeled good and bad parts.
- 04
Line integration
Integrate the inspection system into cycle time and rejection logic.
- 05
Continuous retraining
Continuously improve the model with new defect samples.
Steps
Data sources
Stakeholders
From first data access to production – every step delivers a tangible interim result.
Camera images
High-resolution images from inspection stations.
Labeled defect images
Historical good/bad part examples.
MES order data
Mapping inspection results to orders.
3D scans
Dimensional accuracy for complex geometries.
Complaint data
Feedback on defects that actually occurred.
Lighting and sensor parameters
Conditions for consistent image quality.
Quality management
Gets objective, documented inspection results.
Production management
Increases throughput without sacrificing quality.
Inspection staff
Focuses on complex edge cases instead of routine checks.
Customers/sales
Benefits from consistently low defect rates.
Consistent inspection quality regardless of human factors
Higher throughput at inspection stations
Complete documentation of every inspection
Relief for inspection staff from routine tasks
Earlier detection of emerging defect types
With a solid data foundation this use case gets faster, cheaper and far more stable.
Scalable image processing across multiple lines
Central management and versioning of inspection models
Fast onboarding of new camera stations
Traceable inspection history per part
Predictive quality
Image data improves the prediction of quality issues.
Process parameter optimization
Defect patterns point to underlying process parameters.
Digital twin
Defect data feeds into simulating process variants.