Development of a neural network to recognize standards and features from 3D CAD models

TL;DR

This paper presents a neural network-based method for recognizing standards and features directly from 3D CAD models, enabling detailed identification and information retrieval of machine elements.

Contribution

It introduces a neural network trained to classify nine types of machine parts and integrates API-based data extraction for detailed standard recognition.

Findings

Successfully classified nine machine element classes

Enabled detailed recognition of standardized parts

Integrated API for comprehensive feature extraction

Abstract

Focus of this work is to recognize standards and further features directly from 3D CAD models. For this reason, a neural network was trained to recognize nine classes of machine elements. After the system identified a part as a standard, like a hexagon head screw after the DIN EN ISO 8676, it accesses the geometrical information of the CAD system via the Application Programming Interface (API). In the API, the system searches for necessary information to describe the part appropriately. Based on this information standardized parts can be recognized in detail and supplemented with further information.