Gradient-Based CAD Model Optimization via Sensitivity Analysis
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Solution Overview
Problem
Current CAD model optimization methods for manufacturing products are inefficient, particularly in handling a large number of variables and ensuring manufacturability, as they often rely on combinatorial explosion and exponential computational runtimes, and fail to effectively link performance indicators with design variables.
Innovation Solution
A computer-implemented method using gradient-based optimization that modifies CAD parameters by approximating derivatives of performance indicators with respect to CAD parameters, employing sensitivities and implicit field representations to efficiently optimize CAD models, ensuring manufacturability and handling a high number of design variables.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional combinatorial optimization methods are used for CAD model optimization, then all possible design configurations can be explored, but computational runtime increases exponentially
Solution Approach 1:
The patent transforms the discrete combinatorial optimization problem into a continuous parameter optimization problem by representing CAD model features as continuous parameters. This allows the use of gradient-based optimization methods that compute sensitivities analytically, reducing computational runtime from exponential to polynomial complexity while maintaining optimization effectiveness.
Solution Approach 2:
The patent replaces traditional mechanical/combinatorial search methods with a mathematical field-based approach using implicit representations and gradient computation. By substituting discrete parameter enumeration with continuous field-based sensitivity analysis, the system achieves faster convergence without exhaustive search.
2Adaptability or versatility
If the number of CAD parameters is increased to improve design flexibility, then design capability improves, but computational complexity and difficulty of optimization increases
Solution Approach 1:
The patent introduces an implicit field representation as an intermediary between CAD parameters and performance indicators. This intermediary layer enables efficient computation of sensitivities through the chain rule, allowing the optimization algorithm to handle large numbers of parameters without proportional increases in computational complexity.
Solution Approach 2:
The patent creates a universal optimization framework that can handle any number of CAD parameters through a unified sensitivity computation approach. The implicit field representation and gradient-based method provide a multi-functional solution that scales efficiently regardless of the number of design variables, unlike parameter-specific optimization methods.
3Manufacturing precision
If traditional optimization methods are used, then optimization can be performed, but the link between performance indicators and design variables is weak and ineffective
Solution Approach 1:
The patent implements a feedback mechanism by computing sensitivities that quantify the relationship between CAD parameters and performance indicators. These sensitivity values provide continuous feedback to the optimization algorithm, enabling precise adjustment of design variables to achieve optimal performance while maintaining strong linkage between design decisions and performance outcomes.
Solution Approach 2:
The patent performs preliminary computation of sensitivities before the optimization process begins. By pre-computing the derivative information through implicit field representations, the system prepares sensitivity data that guides the optimization algorithm more effectively, reducing the need for iterative trial-and-error adjustments.
Data Source
AI summary
A computer-implemented method for designing a manufacturing product. The method includes obtaining a CAD model representing the manufacturing product. The CAD model includes a feature tree. The feature tree has one or more CAD parameters each having an initial value. The method also includes obtaining an optimization program. The optimization program is specified by one or more use and/or manufacturing performance indicators. The one or more indicators having one or more objective functions and/or one or more constraints. The method further includes modifying the initial values of the one or more CAD parameters by solving the optimization program using a gradient-based optimization method. The optimization method has as free variable the one or more CAD parameters and uses sensitivities. Each sensitivity is an approximation of a respective derivative of a respective performance indicator with respect to a respective CAD parameter.