Geometric feature extraction in manufacturing based on a knowledge graph

Area of Science:

• Manufacturing Engineering
• Computer-Aided Design (CAD)
• Industry 4.0

Background:

• Global crises highlight the need for resilient production chains.
• Smart manufacturing and Industry 4.0 offer solutions for automated decision-making.
• Computer-Aided Process Planning (CAPP) links design and manufacturing phases.

Purpose of the Study:

• To develop an automatic feature recognition (AFR) method.
• To recognize geometric manufacturing features and properties from 3D models.
• To enable automated process planning by analyzing design data.

Main Methods:

• Extraction of geometric and topological data from 3D models (STEP-AP242 format) using Python scripts.
• Storage of extracted data in an ontology-based knowledge base.
• Analysis of product data to identify manufacturing features with a layered, hierarchical structure.

Main Results:

• Successful development of an automatic feature recognition (AFR) method.
• Creation of a knowledge base storing component features and properties.
• Demonstrated potential for automated analysis and comparison of design with manufacturing information.

Conclusions:

• The developed AFR method facilitates automated process planning.
• Digitalization and linking of product lifecycle phases are crucial for smart manufacturing.
• The approach enhances the efficiency and resilience of production planning.