CAD Feature Trees from Discrete Geometry via Complexity-Penalized Search
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Solution Overview
Problem
Existing solutions lack an efficient method 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, allowing for the generation of a CAD feature tree by determining sequences of CAD features, which includes rewarding fitting and penalizing complexity through a depth-first search in a tree of candidate sequences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to generate CAD feature trees from discrete geometrical representations, then the process can handle simple geometries, but it becomes computationally inefficient and inaccurate for complex mechanical parts
Solution Approach 1:
The patent segments the complex problem of CAD feature tree generation into smaller sub-problems by decomposing the discrete geometrical representation into manageable components and processing them through structured algorithms, thereby improving both accuracy and computational efficiency for complex mechanical parts
Solution Approach 2:
The patent performs preliminary actions by pre-processing the discrete geometrical representation and organizing candidate CAD features before the main generation process, which reduces computational complexity and improves accuracy during the subsequent feature tree construction phases
2Productivity
If existing algorithms are used for CAD feature tree generation, then implementation is straightforward, but the method lacks efficiency and scalability for complex products
Solution Approach 1:
The patent introduces dynamic elements into the CAD feature tree generation process by implementing adaptive algorithms that can adjust their behavior based on the complexity of the input geometry, enabling efficient processing of complex mechanical parts while maintaining scalability
Solution Approach 2:
The patent utilizes parameter changes by adjusting key parameters such as feature selection criteria, tolerance thresholds, and algorithmic control variables during the generation process, which improves productivity and efficiency without requiring fundamentally complex system architecture
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.