CDFAM Computational Design Symposium — Barcelona 2026
Luis Salazar Betancourt · Tech Soft 3D
Advances in computational design and additive manufacturing have enabled increasingly complex geometry, but industrial AI adoption remains constrained by a fundamental challenge: relating CAD design data to manufacturing behavior and downstream business processes. Geometry, process data, and enterprise information are typically analyzed in isolation, making it difficult to understand how design decisions propagate across the product lifecycle.
HOOPS AI addresses this by transforming CAD and manufacturing data into unified, AI-ready representations built on the HOOPS platform. The approach focuses on extracting stable geometric and feature-level abstractions that serve as a common reference across engineering and non-engineering domains, without reliance on native CAD kernels or proprietary data models.
These representations enable systematic comparison, grouping, and retrieval of parts based on geometric and functional characteristics. The same representations can be associated with manufacturing signals, quality indicators, and production constraints, and extended to link with business process information in an IP-safe manner. This allows AI systems to reason about relationships between CAD geometry, manufacturing outcomes, and operational drivers.
The presentation covers the architectural principles behind HOOPS AI and illustrates how unified representations support scalable integration of AI into industrial CAD and additive manufacturing workflows, with emphasis on system design and interoperability.
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