CAD Feature Trees from Discrete Geometry Using Depth-First Search
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Solution Overview
Problem
Existing solutions lack efficient methods for generating a CAD feature tree from a discrete geometrical representation of a mechanical part, particularly for complex products, while maintaining computational efficiency and accuracy.
Innovation Solution
A computer-implemented method using a depth-first search to optimize an objective function that rewards fitting and penalizes complexity, determining sequences of CAD features to generate a CAD feature tree by iteratively adding and pruning candidate sequences based on selection, rollout, and backpropagation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a depth-first search is used to optimize the objective function, then the accuracy of the generated CAD feature tree is improved, but the computational time and complexity increase
Solution Approach 1:
The patent applies preliminary action by pre-defining the search space with a tree of candidate sequences and pre-calculating fitness scores for potential CAD feature sequences. This allows the depth-first search to efficiently navigate towards optimal solutions without exhaustive computation, balancing accuracy with computational time.
Solution Approach 2:
The patent implements feedback mechanisms through the objective function that continuously evaluates candidate sequences based on fitting quality and complexity. The depth-first search uses this feedback to prune branches that cannot lead to optimal solutions, reducing computational time while maintaining high accuracy in generating CAD feature trees.
2Manufacturing precision
If the complexity of the candidate sequence increases, then the fitting accuracy improves, but the overall system complexity increases
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting the complexity threshold and search depth based on the specific mechanical part being analyzed. The objective function modifies the balance between fitting accuracy and sequence complexity according to the problem instance, allowing the system to adapt to different complexity levels without increasing overall system complexity.
Solution Approach 2:
The patent segments the complex task of generating a complete CAD feature tree into smaller sub-tasks by exploring candidate sequences in a tree structure. This segmentation allows the system to manage complexity through hierarchical decomposition, where simpler sub-sequences can be combined to form more complex but accurate feature trees.
Data Source
AI summary
A method for generating a CAD feature tree from a discrete geometrical representation of apart. The method includes obtaining the representation and a set of CAD features. The method includes 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 includes 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.