Quick Start guide: Pundit CMM

Measurement uncertainty is an unavoidable reality in manufacturing and metrology. It’s a hidden cost driver and a critical factor in quality control. That’s where Pundit steps in—a high-level simulation tool designed to quantify and control task-specific uncertainties in dimensional measurements. Whether you're working with complex CAD models or precision CMMs, Pundit helps you understand how much of your tolerance bandwidth is consumed by uncertainty—and whether that’s acceptable.

What Is Pundit and Why Use It?

Pundit isn’t just another metrology tool—it’s a simulation engine that calculates task-specific measurement uncertainties. Instead of relying on generic CMM specs like resolution or accuracy, Pundit dives deeper:

• It evaluates uncertainty per tolerance on the model.

• It accounts for real-world measurement conditions and sampling patterns.

• It answers the key question: How much of the tolerance bandwidth is taken up by uncertainty—and is that acceptable?

Inputs for Pundit Simulation

To simulate measurement uncertainty, Pundit uses five primary inputs:

CAD Geometry

Import geometry from any CAD format with ease and efficiency. Pundit supports Direct Interface (DI) for seamless integration, allowing for a smooth workflow between different systems. It handles PMI (Product Manufacturing Information) import natively, ensuring that all relevant data is accurately captured. PMI can be automatically imported with CAD or manually added in Pundit, providing flexibility based on your specific needs.

Sampling Points

Specify manually or import from CMM software. Enables file-based sampling pattern imports for enhanced flexibility. Emphasize flexibility—Pundit adapts to your workflow.

Additional information about the sampling points can be found in our article📄Sampling points

Form Error

Form error simulation in Pundit allows users to introduce deviations from the ideal geometry of a part, representing the kinds of imperfections that commonly occur during manufacturing. These deviations can be modeled using several available methods, enabling a realistic representation of surface or feature irregularities. By incorporating form errors into the simulation, users can better understand how actual part conditions influence measurement uncertainty and ensure that the analysis reflects true production scenarios.

Environmental factors

Pundit incorporates environmental factors into its uncertainty calculations to ensure realistic and reliable simulation results. These factors include ambient temperature, which can influence both the measurement system and the part being inspected, as well as material-specific properties such as thermal expansion coefficients. By accounting for these variables, Pundit helps users understand how environmental conditions may affect dimensional measurements and ensures that the simulation reflects the actual context in which inspections are performed.

Measurement Resources

In Pundit, the measurement resource defines the equipment and conditions used during inspection. This includes specifications for the CMM, details about the probing system, and relevant environmental parameters. Users can select and configure their CMM using the built-in Measurement Resource Editor, which provides a structured interface for managing machine characteristics. Additionally, probing systems and other measurement components can be defined to reflect the actual setup used in production. These inputs are essential for accurately simulating how the measurement system contributes to overall uncertainty.

Simulation & Reporting

Pundit’s simulation and reporting capabilities provide a comprehensive view of how measurement uncertainty affects each tolerance. Once all relevant error sources are activated—including equipment, environment, probing, and sampling—the simulation calculates the combined impact on measurement results. The output includes a histogram that visualizes the distribution of uncertainty across the tolerance bandwidth. Users can then analyze the contribution of each individual error source, gaining insight into which factors dominate and where improvements may be possible. This detailed reporting supports informed decision-making and enhances confidence in inspection outcomes.

FAQ