Multi-Space Design Concept Optimization via Unified Metric
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Solution Overview
Problem
Current engineering design optimization methods struggle to analyze and identify meaningful design concepts from large datasets spanning multiple description spaces, as they are limited to considering only two description spaces and fail to provide relations between data samples across different spaces, making it complex and computationally intensive to assess and optimize design solutions efficiently.
Innovation Solution
A computer-implemented method that analyzes design data by obtaining a dataset of design variations, determining concept candidates based on feature value similarity, calculating a metric to evaluate concept candidate configurations across multiple description spaces, and selecting representative data samples to generate optimized design concepts, enabling efficient identification and representation of design concepts across arbitrary number of description spaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If optimization methods consider only two description spaces, then computational complexity is reduced, but the ability to provide relations between data samples across different spaces is lost
Solution Approach 1:
The patent extends the analysis from two description spaces to an arbitrary number of description spaces by introducing additional dimensional spaces. Each description space represents a different semantic context or feature category, and the method systematically processes data samples across all these dimensions simultaneously, enabling comprehensive relationship analysis without being limited to only two spaces.
Solution Approach 2:
The patent creates a universal optimization framework that can handle an arbitrary number of description spaces through a single metric evaluation mechanism. The method defines a unified metric that evaluates concept candidate configurations across all description spaces simultaneously, making the system multi-functional and adaptable to different numbers of spaces rather than requiring separate methods for each case.
2Measurement precision
If large datasets are analyzed to identify meaningful design concepts across multiple description spaces, then design concept quality improves, but computational resources required increase
Solution Approach 1:
The patent performs preliminary actions by pre-defining the metric for evaluating concept candidate configurations before the actual optimization process. The metric is formulated to simultaneously evaluate multiple description spaces, and concept candidates are pre-identified based on feature value similarities. This preliminary structuring reduces the computational burden during the main optimization phase by avoiding redundant calculations across multiple spaces.
Solution Approach 2:
The patent changes the parameter representation by introducing a unified metric that aggregates information from multiple description spaces into a single evaluative parameter. Instead of separately processing each description space, the method transforms the multi-space evaluation into a parameter optimization problem where the metric value guides the selection of optimal concept configurations, reducing computational complexity while maintaining precision.
3Adaptability or versatility
If concept candidate configurations are evaluated across multiple description spaces, then design solution comprehensiveness improves, but assessment time increases
Solution Approach 1:
The patent merges the evaluation of multiple description spaces into a single unified metric assessment. Instead of sequentially evaluating each description space separately, the method combines all description space evaluations into one integrated metric calculation that simultaneously considers all spaces. This merging approach maintains comprehensive assessment while reducing the time required by eliminating redundant sequential processing steps.
Data Source
AI summary
A computer-implemented method obtains a dataset including design data samples, each sample representing a design variation of the physical object and including design features, each design feature included in a description space. The method determines concept candidates from the obtained dataset based on at least a feature value similarity of the design features, each concept candidate including a data sample group, for generating concept candidate configurations. The method calculates a metric for said configurations which defines a quality of the generated configurations and evaluates the design features of different description spaces, and evaluates said configurations based on the calculated metric to generate concepts. One or more representative data sample for each concept is determined based on at least one criterion. The determined representative data samples are output. A design process for the physical object based on the output representative data samples for each concept is performed.