Functional restructuring of CAD models for FEA purposes

International audienceDigital Mock-ups (DMUs) are widespread and stand as reference model for product description. However, DMUs produced by industrial CAD systems essentially contain geometric models and their exploitation often requires user's input data to derive finite element models (FEMs). Here, analysis and reasoning approaches are developed to automatically enrich DMUs with functional and kinematic properties. Indeed, geometric interfaces between components form a key starting point to analyse their behaviours under reference states. This is a first stage in a reasoning process to progressively identify mechanical, kinematic as well as functional properties of the components. Inferred semantics adds up to the pure geometric representation provided by a DMU and produce also geometrically structured components and assemblies. Functional information connected to a structured geometric model of a component significantly improves the preparation of FEMs and increases its robustness because idealizations can take place using components' functions and components' structure helps defining sub-domains of FEMs

Generative Object Definition and Semantic Recognition

"What is the difference between a cup and a door?" These kinds of questions have to be answered in the context of digital libraries. This semantic information, which describes an object on a high, abstract level, is needed in order to provide digital library services such as indexing, mark-up and retrieval. In this paper we present a new approach to encode and to extract such semantic information. We use generative modelling techniques to describe a class of objects: each class is represented by one algorithm; and each object is one set of high-level parameters, which reproduces the object if passed to the algorithm. Furthermore, the algorithm is annotated with semantic information, i.e. a human-readable description of the object class it represents. We use such an object description to recognize objects in real-world data e.g. laser scans. Using an algorithmic object description, we are able to identify 3D subparts, which can be described and generated by the algorithm. Furthermore, we can determine the needed input parameters. In this way, we can classify objects, recognize them semantically and we can determine their parameters (cup's height, radius, etc.)