CAD Volume Extrusion Operator Detection via Segmentation

The method segments the CAD 3D model skin into multiple segments representing different skin portions, then iteratively groups these segments to identify extrusion surfaces. This segmentation approach transforms the complex detection problem into manageable sub-problems, enabling efficient identification of extrusion operators by analyzing individual segments and their relationships rather than processing the entire model at once.

The invention introduces objective functions that evaluate segments based on extrusion-axis alignment and closing plane relationships, adding a new dimensional criterion for detection. By computing disparities between segment orientations and extrusion axes, the method creates an additional evaluation dimension that systematically identifies extrusion operators, transforming the detection process from manual inspection to automated mathematical evaluation.

The method performs preliminary segmentation of the CAD model skin into discrete segments before the iterative grouping process. This preliminary action organizes the model data into manageable units with defined properties, enabling the subsequent iterative algorithm to work with pre-processed information rather than raw model data, thereby reducing computational complexity while maintaining detection accuracy.

The iterative grouping process evaluates pairs of segments using objective functions that compute disparities based on extrusion-axis alignment. By focusing computations only on relevant segment pairs and using threshold-based filtering, the method performs partial action on the full set of possible comparisons, achieving accurate detection without exhaustively analyzing every possible segment combination, thus balancing precision with computational efficiency.