Transforming 3D Model Analysis with AI-Powered CAD Geometry Insights

Identifying Void Portions:

• The project faced difficulty in identifying the internal voids within 3D models, which are critical for casting.
• Standard CAD tools did not provide an intuitive way to extract and visualize core voids within the model.

Lack of Direct API Support:

• No existing API provided a direct solution to detect and extract core voids automatically.
• Required a customized approach to generate a separate model that represents the void spaces accurately.

Geometric Complexity:

• Variations in model complexity made it difficult to develop a one-size-fits-all solution for core extraction.
• Some models contained intricate void structures that traditional detection methods could not efficiently handle.

Maintaining Structural Integrity:

• Ensuring that the extracted cores maintained the correct geometric relationships with the original model.
• Avoiding distortions or errors in the extracted void representations.

Computational Efficiency:

• The core extraction process needed to be optimized for performance, as handling large 3D models could lead to slow processing times.
• Balancing accuracy with speed was a key challenge in implementing a scalable solution.