This thesis explores the challenges and methodologies associated with feature extraction from CAD models in robotic 3D printing, focusing on file formats such as STEP, IGES, and STL. The primary objective is to develop a robotic system that can dynamically adjust printing parameters based on realtime temperature feedback, aiming to enhance efficiency and reliability.

The research identifies significant limitations in current CAD-to-robotic 3D printing workflows, particularly the lack of suitable software tools for detailed feature extraction from IGES, STL, and STEP-NC formats. While the STEP File Analyzer software effectively analyzes STEP files, it presents data in a complex and unordered structure, making direct analysis challenging.

The thesis addresses this issue by converting STEP files into JSON format using Python scripting in FreeCAD. The JSON format simplifies data interpretation due to its structured and human-readable nature, allowing for efficient extraction of essential geometric details such as dimensions, coordinates, and other features of CAD models.

The methodology involves creating CAD models in SolidWorks and Creo, converting them into different file formats, and analyzing their feature extraction capabilities.

The study finds that the STEP format excels in preserving geometric and topological data, making it the most suitable for detailed feature extraction in robotic 3D printing. IGES and STL formats, while useful for basic geometric data and direct 3D printing applications, respectively, fall short in handling complex features and maintaining data integrity.

In conclusion, the research underscores the importance of the STEP format for advanced manufacturing processes, highlighting its comprehensive data handling capabilities for CAD model feature extraction in robotic printing. The conversion to JSON format, facilitated by custom Python scripts, further enhances the usability and accessibility of the extracted data.