Camera Setup: Analyze Visibility results

Virtual Visibility Analysis evaluates how well features defined in the Inspection Specification can be observed and measured by all configured cameras. The results help assess measurement robustness, uncertainty contributors, and potential improvements in camera and feature design.

Number of Observations

The Number of Observations indicates how many valid camera observations contributed to the measurement of a feature.

Rule of thumb:

Minimum 6 observations per regular feature
Minimum 8 observations for alignment features

While the number of observations is important, quality beats quantity. A smaller number of high-quality, well-conditioned observations is preferable to many weak or poorly aligned ones.

Low observation counts may indicate:

Limited camera coverage
Features outside the Depth of View (DoV)
Occlusions or poor viewing angles

Cg Values (Cage Capability)

The Cg value is the primary metric for evaluating measurement quality.

Cg is directly comparable to Cg from MSA Study 1
It replaced the older T-QI metric with a more intuitive and standardized measure
Cg expresses measurement uncertainty relative to the specified tolerance range

In other words, Cg indicates how large the measurement uncertainty is compared to the allowed tolerance of the feature. A higher Cg means that the uncertainty consumes a smaller portion of the tolerance band, resulting in a more capable and reliable measurement.

How to Improve Cg

To reduce uncertainty and improve Cg values:

Add more cameras
Change lenses (field of view, focal length)
Improve camera base geometry

Cg values are reported per axis (X, Y, Z), allowing identification of axis-specific weaknesses.

Alignment Effect

The Alignment Effect columns show how much of the total measurement uncertainty comes from the alignment rather than from the feature measurement itself.

Expressed as a percentage
Indicates the contribution of alignment uncertainty to the total uncertainty of a feature

Typical Behavior

Alignment contribution is typically 40–70%
Around 50% on average
Values above 80% are marked red in the table

Interpretation

High alignment effect → alignment measurement dominates uncertainty
Low alignment effect → feature measurement dominates uncertainty

This information tells you where to focus improvement efforts:

Improve alignment measurement accuracy
or
Improve feature measurement quality

Design Implication for Alignment Features

Alignment features must be measured significantly better than regular features.

If alignment is poorly measured:

All features depending on that alignment will inherit higher uncertainty
Overall system performance will degrade

Takeaway:
Always define alignment points in the Inspection Specification tool.
If alignment is not defined, uncertainty values shown in Camera Setup will be deceptively low and not representative of real system performance.

Depth of View (DoV)

Setting the Depth of View (DoV) for each camera is:

Mandatory for systems using Absolute Measurement (AM)
Strongly recommended for traditional systems

Key Rules

Features with blurry patterns cannot be used with Absolute Measurement
Features outside the DoV cone are:
- Filtered out from visibility analysis
- Excluded from pattern creation

Practical Considerations

Higher focal length lenses result in shorter DoV
Closer focus points further reduce DoV
In practice, the highest focal length lens is usually the critical one to monitor

Incorrect DoV settings can lead to:

Missing observations
Reduced Cg values
Incomplete or misleading visibility results

Summary

When analyzing Visibility results:

Ensure sufficient number of observations
Use Cg as the main indicator of measurement quality
Pay close attention to alignment effect percentages
Measure alignment features better than regular features
Always configure Depth of View correctly, especially for Absolute Measurement systems

Proper interpretation of these metrics enables robust inspection design and avoids costly measurement errors later in deployment