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
The system finds existing parts with similar geometry across the company CAD library, enabling reuse instead of redesign from scratch.
Business impact
This can significantly increase reuse, reducing tooling cost, qualification effort, and BOM complexity across the organization.
Limitations
Similarity is based mainly on shape. It does not confirm compatibility in material, tolerance, or manufacturing process, and it performs less reliably on highly organic forms.
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
PTC Windchill AI Parts Rationalization (January 2026) uses geometric deep learning for similar-part search and unification suggestions; pilot projects report 10–20 % unique part number reduction (PTC press release). Physna is also used for cross-project reuse — .