Parametric CAD Model Shape Fitting via Gradient Optimization
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Solution Overview
Problem
Current computer-aided design (CAD) systems struggle to efficiently design manufacturing products with mechanical functionalities based on discrete representation formats, lacking an effective method to optimize CAD models to fit target boundary shapes.
Innovation Solution
A computer-implemented method that modifies CAD model parameters to minimize shape dissimilarity metrics between a CAD model and a target boundary shape, using gradient-based optimization techniques and signed distance fields to adjust continuous CAD parameters, allowing for efficient fitting of a mesh into a parametric CAD model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If discrete representation formats are used for target boundary shapes, then flexibility in representing complex shapes is improved, but efficiency in designing CAD models with mechanical functionalities deteriorates
Solution Approach 1:
The patent introduces an intermediary optimization process that bridges discrete mesh representations and parametric CAD models. The system uses an objective function to quantify shape dissimilarity and employs optimization algorithms to automatically adjust CAD parameters, serving as a mediator that converts between discrete and parametric representations while maintaining both shape fidelity and design efficiency
Solution Approach 2:
The patent systematically modifies CAD model parameters to minimize shape dissimilarity between the target boundary and the CAD representation. By continuously adjusting parameters based on an objective function that measures shape mismatch, the system achieves efficient conversion from discrete representations to parametric models while preserving mechanical functionalities
2Manufacturing precision
If CAD model parameters are manually adjusted to fit target shapes, then shape accuracy is improved, but design time and complexity increase
Solution Approach 1:
The patent implements an automated self-adjusting system where the CAD model automatically optimizes its own parameters to fit the target shape. The optimization algorithm autonomously evaluates shape dissimilarity and adjusts parameters without human intervention, enabling the system to self-correct and achieve high shape accuracy while significantly reducing design time
Solution Approach 2:
The patent establishes a feedback loop where the objective function continuously evaluates the mismatch between target and CAD shapes, and this evaluation feeds back into parameter adjustments. This closed-loop optimization process automatically converges to high-accuracy solutions, eliminating manual trial-and-error and reducing overall design time
3Manufacturing precision
If optimization algorithms are used to minimize shape dissimilarity, then shape matching accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent focuses optimization efforts on a selective subset of critical CAD parameters rather than all parameters, changing only those that most significantly impact shape fidelity. This targeted parameter optimization reduces computational complexity while maintaining high shape matching accuracy by concentrating computational resources on the most influential design variables
Data Source
AI summary
A computer-implemented method for designing a manufacturing product having one or more mechanical functionalities. The method includes obtaining a first instance of a CAD model and a mesh representing a target boundary shape of the manufacturing product and determining a second instance of the CAD model. The CAD model includes a feature tree having a plurality of continuous CAD parameters, and a set of one or more parameterization constraints which specifies the one or more mechanical functionalities. The first instance includes a first value and the second instance includes a second value for each continuous CAD parameter, respectively. The determining of the second instance consists of computing the second values by modifying at least part of the first values to minimize a shape dissimilarity metric between a boundary shape represented by the first instance of the CAD model and the target boundary shape.