Mixed-Level Screening Design Construction for Factor Segmentation
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Solution Overview
Problem
Existing experimental design methods struggle to efficiently account for multiple factors with varying input levels, particularly when limited by a small number of runs, leading to incomplete analysis of factor interactions and potential nonlinear effects.
Innovation Solution
A computer-program product and method that generates a screening design by assigning factors to two-level and three-level groups based on a user-requested run size, allowing for mixed-level screening with at most two input options for categorical factors and at most three input options for continuous factors, enabling the exploration of main and quadratic effects within a reduced number of runs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional screening designs are used with multiple factors having different numbers of candidate inputs, then complete analysis of factor interactions and nonlinear effects can be achieved, but the number of experimental runs required becomes excessively large
Solution Approach 1:
The patent segments factors into different level groups (two-level groups and three-level groups) based on their candidate input counts. This segmentation allows the design to handle mixed-level factors efficiently by treating them as distinct categories, thereby reducing the overall complexity and number of runs required while maintaining analysis completeness for main effects and quadratic effects.
Solution Approach 2:
The patent changes the parameter structure by allowing factors to have different numbers of levels (two-level or three-level) rather than requiring uniform levels across all factors. This parameter change enables more flexible experimental designs that can accommodate mixed-level factors with varying candidate inputs, reducing the exponential growth of runs while preserving the ability to analyze main and quadratic effects.
2Productivity
If the number of experimental runs is reduced to save resources, then resource efficiency improves, but the ability to account for multiple factors with varying input levels deteriorates
Solution Approach 1:
The patent creates a universal screening design framework that can handle multiple factor types (two-level and three-level factors) within a single experimental design structure. This multi-functional approach allows the design to accommodate mixed-level factors with varying candidate inputs while maintaining a reduced number of runs, thereby preserving factor handling capability without sacrificing resource efficiency.
Solution Approach 2:
The patent introduces dynamic flexibility by allowing the experimental design to adapt to different factor configurations. The design can dynamically assign factors to appropriate level groups based on their candidate input counts, enabling the system to handle varying factor complexities while maintaining a fixed, reduced run size that improves resource efficiency.
3Quantity of substance
If mixed-level screening design is implemented with reduced run size, then resource consumption decreases, but design complexity increases
Solution Approach 1:
The patent applies local quality by assigning different level structures (two-level or three-level) to specific factors based on their candidate input counts. Rather than requiring a uniform design structure, the patent allows each factor to have its appropriate local structure, which simplifies the overall design process while maintaining reduced run size, thereby managing design complexity effectively.
Data Source
AI summary
A computing device receives a request requesting a screening design for an experiment. The device obtains factors for screening. A first factor has a first set of candidate inputs comprising at least two candidate inputs for allowable inputs in the experiment. A second factor has a second set of candidate inputs comprising at least three candidate inputs for allowable inputs in the experiment. The device receives an indication of a run size. The device generates the screening design by assigning, based on the run size, the first factor to a two-level group that only allows at most two allowable inputs for the first factor in the experiment. The device generates the screening design by assigning, based on the initial run size, the second factor to a three-level group that only allows at most three allowable inputs for the second factor in the experiment. The device outputs the screening design.