CAD Feature Tree Generation With Depth-First Search

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

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, allowing for the generation of a CAD feature tree by determining sequences of CAD features, which includes a depth-first search in a tree of candidate sequences based on rewarding fitting and penalizing complexity.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If a discrete geometrical representation is used for complex mechanical parts, then the accuracy and fidelity of the geometrical representation is improved, but the complexity of generating the CAD feature tree increases

Engineering Contradiction:

Improveaccuracy of geometrical representationVSAvoidcomplexity of CAD feature tree generation

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent segments the complex problem of generating CAD feature trees into smaller sub-problems by identifying and processing individual CAD features (such as extrusions, revolutions, cuts) within the discrete geometrical representation. This segmentation allows the system to handle complex geometries by breaking them down into manageable feature-level operations, thereby reducing the overall complexity of the generation process while maintaining accuracy.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent utilizes parameter changes by adjusting various parameters of CAD features (such as dimensions, positions, orientations) to accurately represent the discrete geometrical data. By optimizing these parameters, the system can maintain high accuracy in the geometrical representation while managing the complexity through systematic parameter adjustment rather than complex structural changes.

Inventive Principle:

Principle #35Parameter changes

2Productivity

If traditional methods are used to generate CAD feature trees, then the process is simpler, but the time required for generation increases

Engineering Contradiction:

Improvespeed of CAD feature tree generationVSAvoidtime required for generation

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The patent applies preliminary action by pre-processing the discrete geometrical representation to identify potential CAD features and their parameters before actual feature tree construction. This preliminary analysis stage prepares the data structure and identifies feature candidates, which significantly accelerates the subsequent feature tree generation process while maintaining systematic simplicity.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent replaces traditional mechanical or manual methods of CAD feature tree generation with an automated computational system that uses algorithms to identify and construct features from discrete geometrical data. This substitution of automated processing for manual or traditional methods dramatically reduces generation time while maintaining a relatively simple procedural framework.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Productivity

If the complexity of the candidate sequence is penalized, then the computational efficiency is improved, but the fitting accuracy may be reduced

Engineering Contradiction:

Improvecomputational efficiencyVSAvoidfitting accuracy

Core Design Contradiction:

ProductivityVSManufacturing precision

Solution Approach 1:

The patent employs parameter changes by adjusting the penalty weight for complexity in the objective function to find an optimal balance between computational efficiency and fitting accuracy. By systematically varying this parameter, the system can adapt to different requirements, achieving high computational efficiency without sacrificing necessary fitting accuracy for the specific application context.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentEP4632616A1Generating a CAD feature tree from a discrete geometrical representation of a mechanical part

Publication Date: 2025.10.15 DASSAULT SYSTEMES SA

Figure 1~2
Figure 3
Figure 4

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.