Engineering Autonomy Robustness Scoring With Stratified Sampling
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Solution Overview
Problem
Current methods for evaluating the robustness of engineering products, such as derivative approaches and Monte Carlo-based sampling, face limitations including excessive conservatism, local solutions, scalability issues, and inefficiency in handling non-normal distributions, leading to suboptimal engineering calculations and resource-intensive computations.
Innovation Solution
A computer-implemented method using optimal stratified sampling algorithms to generate probabilistic samples across multidimensional engineering domains, selecting combinations of higher fidelity models and response surfaces to determine robustness scores, and presenting these through a graphical user interface for improved decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If Monte Carlo sampling is used to evaluate robustness, then comprehensive coverage of the design space is achieved, but computational resources are excessively consumed and scalability deteriorates as dimensions increase
Solution Approach 1:
The multidimensional design space is divided into multiple strata or layers based on the distribution characteristics of input variables. This segmentation allows the sampling process to focus computational resources on critical regions while maintaining comprehensive coverage, thereby improving both accuracy and efficiency simultaneously
Solution Approach 2:
The sampling strategy dynamically adjusts parameters such as sample size and stratification levels based on the dimensionality of the problem and the desired confidence level. This adaptive parameter adjustment enables the method to maintain robustness evaluation accuracy while scaling efficiently to high-dimensional problems
2Measurement precision
If derivative approaches are used to analyze robustness, then local sensitivity metrics are obtained, but the assumption of differentiability and continuity limits validity to local solutions only
Solution Approach 1:
The design space is partitioned into multiple local regions, and derivative-based sensitivity analysis is performed within each region. By segmenting the domain, the method maintains the precision of local derivative measurements while extending applicability to the entire engineering domain through systematic coverage of all regions
Solution Approach 2:
The approach integrates multiple analysis techniques (derivative-based local analysis and sampling-based global analysis) into a unified framework. This multi-functional system can handle both differentiable and non-differentiable problems, making it universally applicable across the entire engineering domain rather than limited to specific local regions
3Reliability
If factors of safety are used to minimize failure likelihood, then robustness against variations is improved, but excessive conservatism is introduced leading to suboptimal engineering calculations
Solution Approach 1:
The system uses sampling results to provide feedback on actual system performance and failure probabilities. This feedback replaces arbitrary safety factors with data-driven reliability assessments, reducing excessive conservatism while maintaining robustness. The feedback loop enables continuous refinement of design decisions based on actual performance data
Solution Approach 2:
The method substitutes the traditional mechanical approach of applying safety factors with a probabilistic sampling-based assessment system. This replacement eliminates the need for conservative safety margins by directly evaluating failure probabilities through systematic sampling, thereby simplifying engineering calculations while maintaining or improving reliability
Data Source
AI summary
Methods and systems are provided for evaluating robustness of engineering components. An example method includes receiving an engineering problem definition for a multidimensional engineering domain of an engineering component, in which the engineering problem definition includes a description of a plurality of engineering features of the engineering component. Executing an optimal stratified sampling algorithm that obtains a plurality of probabilistic samples from the multidimensional engineering domain. Selecting optimal combinations of higher fidelity models, reduced order models and response surfaces for execution of the probabilistic samples. Executing the probabilistic samples using the selected combinations to determine a respective engineering response for each engineering feature of the plurality of engineering features. Generating a robustness scorecard for the engineering component based on the determined engineering responses to quantify a robustness score for each engineering feature of the plurality of engineering features. Presenting the robustness scorecard to a user.