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Autonomous part ID
Match a live camera image to CAD-generated views. Edge and shape cues identify parts that confuse conventional vision.
Vision · Metrology · Autonomy
AI-Driven Measurement for Modern Manufacturing
We turn standard coordinate measuring machines into self-driving inspection systems — identifying parts, selecting programs, and running measurements with far less manual work.
The bottleneck
Inspection still waits on people the rest of the line does not.
On most shop floors a CMM is precise — and slow to start. Operators still identify the part, pick the program, and set it up. That creates delays, mix-ups, and idle spindles while skilled metrology talent is already scarce.
What changes
- Live camera view of the part on the CMM
- AI identification even for textureless machined parts
- Automated probe alignment from actual part placement
- Automatic inspection routine selection
- Hands-off measurement, including lights-out shifts
- Works on new machines or as a retrofit
Capabilities
Built for the measurement cell, not a demo bench.
Computer vision, machine learning, and robotics sit on top of the CMM workflow you already trust — so quality can keep pace with production.
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Program selection
Once the part is known, the correct inspection routine is chosen and started without a hunt through folders on the controller.
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New or existing CMMs
Integrate on new equipment, or add a low-cost vision kit to machines already on the floor.
How it works
From camera frame to measured part.
1
See
A camera captures the part in the measurement volume under shop-floor lighting.
2
Identify
Models compare live imagery with CAD views and recover identity from edges and form.
3
Align
Automated probe alignment from how the part actually sits — usually the slowest manual step on a CMM.
4
Measure
The matching CMM program runs. Feedback flags anomalies during the cycle.
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Close the loop
Results feed quality and, over time, adaptive manufacturing — not a stack of printouts.
Industries
Where missed dimensions are expensive.
Aerospace
High-mix precision parts that cannot wait on a specialist for every setup.
Medical devices
Repeatable inspection with a smaller chance of the wrong program on the wrong part.
Automotive
Throughput on the CMM that closer matches the pace of machining and assembly.
Research
NSF SBIR-backed autonomous metrology.
Optic Fringe Corp. is developing this capability with U.S. National Science Foundation SBIR Phase I and Phase II support — taking part identification from research into shop-floor systems.
Phase II
NSF SBIR award for autonomous CMM measurement
CAD + live
Solid models and camera images in one identification stack
Retrofit
Vision on machines you already own, not only new capital
Massachusetts
Built for U.S. manufacturing
Next step
Ready to discuss your CMM cell?
Tell us about the machines, part mix, and inspection bottleneck. We will follow up on pilots, retrofits, and integration with equipment builders.
Contact Optic Fringe