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Quality & Testing · Production
ML for Engineering / Deep Learning on Geometry
Inline visual and geometric quality inspection
Live Core high effect
Core capability
The system helps teams find similar parts, detect geometric issues, and work more effectively with large 3D datasets used in inspection, reuse, and engineering search.
How it works
The system turns each 3D shape into a compact digital fingerprint, making it possible to quickly compare new parts or scans against large databases and identify matches, anomalies, or likely defects.
Application here
Camera and 3D inspection systems automatically check every part on the production line for defects and dimensional deviations.
Business impact
This enables consistent, high-throughput inspection that catches issues earlier and reduces downstream quality costs.
Limitations
It needs strong defect examples and stable inspection conditions. It cannot detect every defect type and does not replace destructive or internal inspection for critical features.
In production
This is already useful for avoiding duplicate parts, speeding up inspection, and making large geometry libraries easier to search and reuse.
Research
The frontier is a more universal 3D intelligence layer that understands shape broadly enough to support many tasks with far less custom retraining for each new use case.
Examples
Deep Learning of 3D Point Clouds for Detecting Geometric Defects in Gears
Description: Researchers adapt a PointNet++‑style deep‑learning model to 3D point clouds from gear scans, enabling automated end‑to‑end classification of gear designs and multi‑class geometric defect detection on a production‑relevant dataset while achieving near‑perfect design classification and high inspection accuracy.



Detecting Teeth Defects on Automotive Gears Using Deep Learning
Description: A vision system for automotive gear inspection uses Faster R‑CNN to detect tooth‑level defects from 2D/3D images, reducing manual inspection of non‑defective gears by about two‑thirds in a realistic plant‑scale setup.



Intelligent Inspection Method and System of Plastic Gear Surface Defects
Description: A deep‑learning‑based online inspection system for plastic gears is deployed on a high‑volume production line, inspecting over 60,000 gears per day, classifying multiple defect types with high mAP and enabling automatic sorting without a specific vendor‑focused product.
https://www.sciencedirect.com/science/article/pii/S2213846324002438https://pmc.ncbi.nlm.nih.gov/articles/PMC8707117/https://lnkd.in/gKrxdVG4
Sources
Cognex ViDi — ; Landing AI —
https://www.cognex.com/products/deep-learninghttps://landing.ai/