Manufacturing

AI defect detection and automatic rejection

Nara finds defects such as cracks, chips, scratches, dents, discolouration, wet spots and missing seals, and signals your PLC or rejector to remove the item from the line. An image of every defect is saved for review and for retraining the model.

Trained on your itemsNara Reva

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The same camera view before detection — Defect detection on a printed circuit board. Move the lens to compare the raw camera view with what Nara detects.

Defect detection on a printed circuit board. Move the lens to compare the raw camera view with what Nara detects.

[ 01 ]The problem

Defects must be removed from the line automatically, at line speed, without stopping production.

01inputCamera and part sensor on the line
02processDefect model trained in Reva on your own parts
03actionSignal the PLC or rejector, save the defect image, update the defect-rate dashboard

How it works

A model trained in Reva on images of your good and defective parts runs in a Nara flow. When it finds a defect, Nara signals the PLC or rejector over Modbus or TCP/IP to remove the item, saves the image, and updates the defect rate on the dashboard. Saved images go back into Reva to improve the model over time.

We train a model on your own products or defects in Reva, and Nara runs it on the line.

What you need

A camera and part sensor at the inspection point
A PLC or rejector reachable over Modbus or TCP/IP
Images of good and defective parts to train a model in Reva

Nara building blocks

Camera nodes
Custom defect model (Reva)
Modbus and TCP/IP to PLC
Image export
Database table
Dashboard with defect rate

What it improves

Defect escape rate
Reject accuracy (fewer false rejects)
Throughput kept at line speed

[ 03 ]Where it runs

Proof from real deployments.

A rubber products manufacturer

Wet rubber clumps rejected before they move downstream

White pellets moving along an enclosed conveyor

A conveyor system operator

AI vision added to an existing conveyor

FAQ

Questions, answered.

What kinds of defects can it find?

Cracks, chips, scratches, dents, discolouration, wet spots, missing seals and other visible defects, once a model has been trained in Reva on images of your parts.

How does the model improve over time?

Nara saves an image of every defect. Those images go back into Reva, where the model is retrained and redeployed to Nara as a Nabrio Model (NAM) file.