Symmetric Random Scatter Process for Product Design Optimization
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Solution Overview
Problem
Current product design systems are limited in optimizing multiple competing design requirements simultaneously, hiding underlying relationships and interactions between design parameters and responses, and are inefficient due to reliance on slow simulation tools and single-point solutions.
Innovation Solution
A method and system that generate computational models to represent interrelationships between input and output parameters using a symmetric random scatter process, enabling statistical distribution generation and optimization of input parameters to maximize compliance with multiple output requirements, while reducing computational load and providing insight into causal relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If multiple design requirements are transformed into a single goal function for optimization, then optimization can be performed, but the underlying relationships and interactions between design parameters and responses are hidden
Solution Approach 1:
The patent segments the single goal function into multiple independent objective functions, each representing a specific design requirement. This allows the optimization system to evaluate and optimize multiple competing requirements simultaneously while maintaining visibility of individual parameter relationships and interactions.
Solution Approach 2:
The patent transitions from a single-dimensional optimization approach to a multi-dimensional objective space. By representing multiple design requirements as separate dimensions (objectives), the system preserves the relationships between design parameters and responses across multiple dimensions rather than collapsing them into one.
2Productivity
If traditional optimization systems are used, then single-point solutions are obtained, but these solutions are unstable when subject to variability introduced by manufacturing processes
Solution Approach 1:
The patent implements a dynamic optimization approach that incorporates variability and uncertainty into the optimization process. Instead of seeking a fixed single-point solution, the system identifies regions of design space that maintain performance across varying conditions, making the solution adaptive and robust to manufacturing variability.
Solution Approach 2:
The patent applies prior cushioning by pre-accounting for manufacturing variability and other sources of uncertainty during the optimization process. The system identifies design solutions that inherently buffer against expected variations, ensuring stability before actual manufacturing occurs rather than requiring post-manufacturing adjustments.
3Measurement precision
If slow simulation tools are used to generate each new model result, then accurate results are obtained, but computational efficiency is reduced
Solution Approach 1:
The patent performs preliminary actions by pre-processing design data, identifying patterns and relationships, and creating surrogate models or approximations before the main optimization process. This preliminary preparation enables faster evaluation of design alternatives while maintaining accuracy through the use of pre-computed information and intelligent data structures.
Solution Approach 2:
The patent creates simplified copies or surrogate models of the complex simulation tools. These surrogate models capture the essential input-output relationships of the detailed simulations but can be evaluated much faster, allowing for efficient exploration of design space while preserving the accuracy characteristics of the original simulation-based approach.
Data Source
AI summary
A method is provided for designing a product. The method may include obtaining data records relating to one or more input variables and one or more output parameters associated with the product and selecting one or more input parameters from the one or more input variables. The method may also include generating a computational model indicative of interrelationships between the one or more input parameters and the one or more output parameters based on the data records and providing a set of constraints to the computational model representative of a compliance state for the product. Further the method may include using the computational model and the provided set of constraints to generate statistical distributions for the one or more input parameters based on a symmetric random scatter process and the one or more output parameters. The one or more input parameters and the one or more output parameters represent a design for the product.