Optical Inspection

Understanding optical inspection system components:

Cameras:

Area Scan:
- 2D sensor captures full frame
- Matrix of pixels (e.g., 2048 x 2048)
- Inspects stationary or triggered
- Most common type

Line Scan:
- Single row of pixels
- Builds image as object moves
- Continuous web inspection
- Very high resolution possible

3D Cameras:
- Structured light, stereo, time-of-flight
- Surface height measurement
- Volume and shape inspection
- Increasingly affordable

Sensor Types:
- CCD: Higher quality, lower speed
- CMOS: Faster, lower power
- Global shutter: Freeze motion
- Rolling shutter: Careful with motion

Resolutions:
- From VGA (640x480) to 100+ megapixels
- Higher resolution = more detail but more data
- Match to feature size requirements

Lighting:

Critical for consistent imaging:

Lighting Techniques:
- Bright Field: Light from same side as camera
- Dark Field: Light from low angle, highlights surface defects
- Backlighting: Silhouette for edge detection
- Diffuse Dome: Even, shadow-free illumination
- Structured: Patterns for 3D measurement

Light Sources:
- LED: Most common, long life, controllable
- Strobe: Freeze motion, high intensity
- Halogen: Broad spectrum, older technology
- Laser: Line generators, specific applications

Color vs. Monochrome:
Monochrome often preferred:
- More sensitive (no Bayer filter)
- Simpler processing
- Use color only when needed
- Colored lighting for contrast

Lenses:
- Fixed focal length (most industrial)
- Telecentric (eliminate perspective distortion)
- Macro (close-up, small features)
- Field of view and working distance determine selection

Common machine vision inspection tasks:

Presence/Absence:
Verify components present:
- Missing parts detection
- Label verification
- Orientation check
- Simplest applications

Measurement (Gauging):
Dimensional verification:
- Length, width, diameter
- Edge-to-edge distances
- Sub-pixel accuracy possible
- Calibration essential

Defect Detection:
Find visual anomalies:
- Surface scratches, stains
- Cracks and chips
- Color variations
- Most challenging applications

Pattern Matching:
Find and locate features:
- Part localization
- Alignment verification
- Character recognition
- Barcode/2D code reading

Color Analysis:
Verify color correctness:
- Color sorting
- Print verification
- Product consistency
- Requires color camera

Industry Applications:

Automotive:
- Body panel inspection
- Engine component verification
- Assembly verification
- Paint defect detection

Electronics:
- PCB inspection (AOI)
- Component placement verification
- Solder joint inspection
- Connector inspection

Pharmaceutical:
- Label verification
- Fill level checking
- Particle detection
- Blister pack inspection

Food and Beverage:
- Foreign object detection
- Label inspection
- Fill level
- Package integrity

Semiconductor:
- Wafer inspection
- Die inspection
- Package inspection
- Highest resolution requirements

Developing inspection applications:

Vision Software Platforms:

Commercial Integrated:
- Cognex VisionPro, In-Sight
- Keyence vision systems
- SICK AppSpace
- Complete solution packages

Open/Configurable:
- National Instruments Vision
- MVTec HALCON
- OpenCV (open source)
- More flexibility, more expertise needed

Smart Cameras:
- Self-contained systems
- Embedded processing
- Easy deployment
- Limited customization

Programming Approach:

Typical Workflow:
1. Acquire image (triggering, exposure)
2. Pre-process (filtering, enhancement)
3. Locate part (pattern matching)
4. Measure/inspect features
5. Make pass/fail decision
6. Communicate results

Image Processing Tools:
- Edge detection
- Blob analysis
- Pattern matching
- OCR/barcode reading
- Color analysis
- Morphological operations

AI and Deep Learning:
Increasingly important:
- Defect classification
- Complex pattern recognition
- Anomaly detection
- Learning from examples
- Cognex ViDi, HALCON Deep Learning

Development Best Practices:

Image Quality First:
- Optimize lighting before software
- Consistent imaging conditions
- Proper camera/lens selection
- Environment control

Robust Algorithms:
- Handle normal variation
- Avoid false rejects
- Stable thresholds
- Test with real production variation

Validation:
- Test with known good/bad parts
- Statistical capability study
- Gage R&R for measurement
- Ongoing monitoring

Machine vision expertise is highly sought:

Vision Technician:
- System operation and maintenance
- Recipe changes
- Basic troubleshooting
- $50,000-$70,000

Vision Engineer:
- System design and programming
- Application development
- Integration with automation
- $75,000-$110,000

Vision Specialist:
- Complex application development
- Deep learning implementation
- System architecture
- $90,000-$130,000

Applications Engineer:
- Vendor position
- Customer support
- Pre-sales technical support
- $70,000-$100,000

Skills Development:

Technical Foundations:
- Optics and lighting
- Camera technology
- Image processing concepts
- Programming (often C#, Python)

Platform Experience:
- Major vendor platforms
- Deep learning tools
- Integration with PLCs
- Robot vision

Manufacturing Knowledge:
- Process understanding
- Quality requirements
- Production environment realities

Training Resources:
- Vendor training programs
- Online courses (vision fundamentals)
- Hands-on project experience
- Industry conferences (AIA Vision Week)

Career Progression:

Path 1: Manufacturing
Technician -> Engineer -> Senior Engineer -> Manager

Path 2: System Integration
Technician -> Programmer -> Project Lead -> Business Development

Path 3: Vendor
Field Support -> Applications Engineer -> Product Manager

Industries:
- Automotive
- Electronics manufacturing
- Pharmaceutical
- Food and beverage
- General manufacturing

Trends:
- Deep learning expanding applications
- 3D vision becoming mainstream
- Embedded/edge processing
- Vision-guided robotics growing

Machine vision skills increasingly valuable as inspection automation expands.