Industrial Intelligence A structured map of industrial AIA structured map of industrial AI
LinkedIn
Technology
Status
Fit
Effect
Hover any cell to preview
Design & R&D · Design
ML for Engineering / Deep Learning on Geometry
Geometry similarity search during design reuse
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
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
AI based geometric similarity search supporting component reuse in engineering design
Description: A CIRP‑design‑conference paper demonstrates an ML‑driven geometric similarity search that encodes CAD models into class‑specific feature vectors; tests on a real mechanical‑engineering dataset show fast retrieval of similar CAD models and improved reuse compared with manual or text‑based search.



Methods for investigating shape‑based similarity in CAD models (Epiroc case)
Description: A KTH‑affiliated thesis applies shape‑based similarity retrieval to Epiroc’s 3D CAD parts, using ML‑encoded shape descriptors to identify duplicates and similar components in a standard‑parts dataset, enabling reuse and reducing unnecessary variation in mechanical design.



AI-Based Retrieval to Encourage Reuse of CAD‑Designs
Description: A NordDesign 2024 study describes a UV‑Net‑based deep‑learning framework that converts CAD solids into tensors for shape‑aware retrieval, tested on industrial CAD datasets to retrieve similar parts and support reuse‑driven design sessions.
https://publikationen.bibliothek.kit.edu/1000148974https://kth.diva-portal.org/smash/get/diva2:1871128/FULLTEXT01.pdfhttps://www.designsociety.org/download-publication/47622/AI-Based+Retrival+to+Encourage+Reuse+of+CAD-Designs:+A+Methodological+S
Sources
PTC Windchill AI Parts Rationalization — ; Physna —
https://www.ptc.com/en/news/2026/ptc-launches-windchill-ai-parts-rationalizationhttps://physna.com/