Probabilistic Modeling 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 between design parameters and product responses, and are inefficient due to reliance on slow simulation tools and inability to handle stochastic variability and multi-dimensional optimizations.
Innovation Solution
A method and system that preprocesses data records to generate statistical distributions for input and output parameters, using computational models to optimize product designs based on probabilistic modeling, allowing for the identification of interrelationships and causal relationships between design inputs and outputs, and reducing computational load by selecting relevant input parameters and using neural networks for optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
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 product responses are hidden
Solution Approach 1:
The patent segments the optimization problem by maintaining multiple separate goal functions corresponding to different design requirements (stress, strain, vibration, etc.) rather than combining them into a single function. This allows the system to evaluate and optimize each requirement independently while preserving the distinct relationships between design parameters and each specific product response.
2Manufacturing precision
If domain-specific optimization algorithms are used, then single requirement optimization is achieved, but multiple competing design requirements cannot be optimized simultaneously
Solution Approach 1:
The patent creates a universal optimization system that can handle multiple competing design requirements simultaneously by implementing a multi-objective optimization framework. The system evaluates multiple goal functions (stress, strain, vibration response, modal frequencies, stability) concurrently and finds design configurations that balance all requirements, making the optimization process adaptable to various competing objectives without being limited to a single domain-specific algorithm.
3Measurement precision
If slow simulation tools are used to generate each new model result, then accurate analysis is obtained, but computational efficiency is reduced
Solution Approach 1:
The patent applies preliminary action by pre-processing data records and establishing heuristic models between design inputs and outputs before the optimization process begins. The system collects desired patterns of design inputs and pre-computes relationships, creating a database of pre-analyzed configurations. During optimization, the system queries this pre-computed information rather than running full simulations for each evaluation, significantly reducing computational load while maintaining accuracy.
4Productivity
If single point solutions are provided, then optimization results are obtained, but the solutions may be unstable when subject to stochastic variability
Solution Approach 1:
The patent transforms the optimization output from single point solutions to statistical distributions of design parameters. Instead of providing a single optimal value for each design parameter, the system computes probability distributions that capture the variability and uncertainty in the optimization results. This allows designers to understand the range of possible outcomes and select parameter values that are robust to stochastic variability in manufacturing and operation, thereby improving solution reliability.
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 pre-processing the data records based on characteristics of the input variables. The method may also include selecting one or more input parameters from the one or more input variables; and 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. Further, the method may include providing a set of constraints to the computational model representative of a compliance state for the product; and using the computational model and the provided set of constraints to generate statistical distributions for the one or more input parameters and the one or more output parameters, wherein the one or more input parameters and the one or more output parameters represent a design for the product.