CAD Feature Tree Optimization via Objective Function Balancing
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Solution Overview
Problem
Current methods for generating CAD feature trees from discrete geometrical representations of mechanical products are inefficient and lack insight into feature generation, leading to poor computation efficiency and limited application on standard 3D models.
Innovation Solution
A computer-implemented method that optimizes an objective function to determine sequences of CAD features by rewarding fitting to the discrete geometrical representation while penalizing complexity, allowing for the generation of CAD feature trees that balance accuracy and complexity, using a weighting parameter to control the trade-off between fitting and complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If generic methods for CSG tree reconstruction are used, then the method can be applied to general 3D models, but the computation efficiency is poor and the method lacks insight into CAD feature generation
Solution Approach 1:
The patent applies local quality by treating different regions of the 3D model with different processing strategies. Specifically, it identifies and processes CAD-specific features (such as extrusions, revolutions, and sweeps) differently from generic geometric elements. This allows the method to optimize for CAD feature recognition in relevant regions while maintaining general applicability for other regions, thereby improving computation efficiency without sacrificing versatility.
Solution Approach 2:
The patent changes parameters by introducing CAD-specific constraints and parameters into the optimization process. It modifies the objective function to include terms that reward CAD feature conformity and penalize complexity, transforming the generic CSG reconstruction problem into a CAD-aware optimization problem. This parameter change enables faster convergence for CAD models while maintaining the method's applicability to general 3D models.
2Measurement precision
If the number of CAD features in the sequence is increased to improve fitting accuracy, then the fitting quality improves, but the complexity of the candidate sequence increases
Solution Approach 1:
The patent implements feedback through its optimization framework, which continuously evaluates the trade-off between fitting accuracy and sequence complexity. The objective function provides feedback by rewarding sequences that achieve good fitting while penalizing those with excessive complexity. This feedback mechanism guides the search process to converge on solutions that balance accuracy and complexity, preventing the unbounded increase of features.
Solution Approach 2:
The patent changes parameters by introducing a weighting parameter in the objective function that controls the trade-off between fitting accuracy and complexity. By adjusting this parameter, the method can adaptively balance the number of features against the quality of reconstruction, finding an optimal point that satisfies both requirements without unnecessarily increasing sequence complexity.
Data Source
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AI summary
The disclosure notably relates to a computer-implemented method for generating a CAD feature tree from a discrete geometrical representation of a mechanical product. The method comprises providing the discrete geometrical representation, and a set of CAD features. The method further comprises determining one or more sequences of CAD features from the set of CAD features by optimizing an objective function which rewards a fitting of the discrete geometrical representation by a candidate sequence, and penalizes a complexity of a candidate sequence, the complexity of a candidate sequence being a function of the candidate sequence that increases when adding a feature to the candidate sequence.