CAD Feature Tree Reconstruction From Discrete Part Geometry
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Solution Overview
Problem
Existing solutions for generating CAD feature trees from discrete geometrical representations of mechanical parts are inadequate in terms of accuracy, complexity control, and computational efficiency, particularly for complex mechanical products.
Innovation Solution
A computer-implemented method that optimizes an objective function to generate CAD feature trees by performing a depth-first search, rewarding fitting to the discrete geometrical representation while penalizing complexity, using a set of CAD features and employing depth-first search to efficiently determine sequences that balance accuracy and complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If evolutionary algorithms or binary optimization techniques are used to generate CAD feature trees, then the fitting accuracy to discrete geometrical representation is improved, but the computational time and complexity increase significantly
Solution Approach 1:
The patent applies preliminary action by performing a depth-first search to pre-identify and organize candidate CAD features and their hierarchical relationships before optimization. This preliminary structuring of the feature tree reduces the search space for subsequent optimization algorithms, enabling faster convergence to accurate solutions without exhaustive computation.
Solution Approach 2:
The patent segments the complex optimization problem into distinct phases: (1) depth-first search to build candidate feature sequences, (2) objective function evaluation to assess fitting accuracy and complexity, and (3) iterative optimization to refine the feature tree. This segmentation allows each phase to be optimized independently, reducing overall computational burden while maintaining high fitting accuracy.
2Manufacturing precision
If more CAD features are added to the feature tree to improve fitting accuracy, then the fidelity to the discrete geometrical representation increases, but the complexity of the candidate sequence increases
Solution Approach 1:
The patent implements feedback through an objective function that continuously evaluates both the fitting accuracy and the complexity of the candidate feature sequence. This feedback mechanism allows the optimization process to identify the optimal point where adding more features no longer provides proportional accuracy improvements, thereby preventing unnecessary complexity while maintaining adequate fidelity.
Solution Approach 2:
The patent changes parameters by dynamically adjusting the selection and ordering of CAD features based on their contribution to fitting accuracy versus complexity. The depth-first search explores different parameter combinations of feature sequences, and the objective function guides parameter selection to achieve the best balance between fidelity and simplicity.
3Measurement precision
If exhaustive search methods are used to find the optimal CAD feature sequence, then the accuracy of the generated feature tree is maximized, but the computational efficiency decreases
Solution Approach 1:
The patent performs preliminary action by using depth-first search to pre-organize candidate features into hierarchical sequences before optimization. This preliminary structuring identifies promising feature combinations early, allowing the subsequent optimization to focus only on refined adjustments rather than exhaustive exploration of all possible sequences, thus maintaining accuracy while improving efficiency.
Data Source
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AI summary
The disclosure relates to a method for generating a CAD feature tree from a discrete geometrical representation of apart. The method comprises providing the representation. The method comprises providing a set of CAD features. The method comprises optimizing an objective function rewarding a fitting of the discrete geometrical representation by a candidate sequence of CAD features and penalizing a complexity of the candidate sequence. The complexity of the candidate sequence is a function of the candidate sequence that increases when adding a feature to the candidate sequence. The optimization comprises determining one or more sequences of CAD features from the set of CAD features, including performing a depth-first search in a tree of candidate sequences of CAD features based on rewarding a fitting of the discrete geometrical representation by a candidate sequence and on penalizing a complexity of a candidate sequence.