Experiment Design Interface for Custom Model Types and Design Runs
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Solution Overview
Problem
Existing experimental design tools lack the ability to efficiently integrate user-defined objectives and model types, limiting the flexibility and accuracy of experimental design and analysis.
Innovation Solution
A computer-program product that allows users to input factor identities, response identities, and objectives through a graphical user interface, displaying model types and design runs, enabling selection of a user-defined model type and amount of design runs, and generating the experiment based on construction criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing experimental design tools use fixed model types and design runs, then the tool structure is simple, but the flexibility and accuracy of experimental design are limited
Solution Approach 1:
The tool dynamically adapts its interface and processing logic based on the user's selected usage mode (guided vs. flexible). In guided mode, the system provides automated defaults and simplified workflows, while in flexible mode, it enables detailed customization of model types, design runs, and construction criteria. This dynamic behavior allows the same tool to serve both novice users seeking simplicity and expert users requiring flexibility.
Solution Approach 2:
The experimental design tool is designed to perform multiple functions across different usage scenarios. It can operate in both guided mode (providing automated, simplified design generation) and flexible mode (allowing detailed customization of all design parameters). This multi-functionality enables a single tool to replace multiple specialized tools, achieving versatility without proportionally increasing complexity.
2Measurement precision
If existing tools use automated default settings, then the ease of operation is high, but the precision of experimental outcomes is reduced
Solution Approach 1:
The system dynamically adjusts the level of automation and user control based on the selected usage mode. In guided mode, automated defaults provide ease of operation for quick prototyping. In flexible mode, the system transitions to manual control with detailed parameters for model types, design runs, and construction criteria, enabling precise experimental design when needed.
Solution Approach 2:
The tool interface is segmented into distinct operational modes (guided and flexible), each serving different user needs. The guided mode segment provides high-level automated functionality for users prioritizing ease of use, while the flexible mode segment offers granular control over design parameters for users prioritizing precision. This segmentation allows users to select the appropriate level of detail without being overwhelmed by all options simultaneously.
3Manufacturing precision
If the tool provides multiple model types and customization options, then the accuracy of experimental design improves, but the device complexity increases
Solution Approach 1:
The interface complexity dynamically adjusts based on the selected usage mode. In guided mode, only essential parameters are displayed with automated defaults, maintaining simplicity. In flexible mode, the full range of model types, design run configurations, and construction criteria become available, enabling accurate experimental design when the user has the expertise to utilize these options.
Solution Approach 2:
Different parts of the interface provide different levels of detail and control based on the selected usage mode. The guided mode presents a simplified view with localized automation for common tasks, while the flexible mode reveals detailed configuration options for specific parameters. This local quality approach ensures that complexity is only exposed where and when it is needed for accurate experimental design.
Data Source
AI summary
A computing device receives user input for a design of an experiment. The user input indicates respective factor identities for factors in the design of the experiment, respective response identities for responses to options for the factors in the design, and a user-defined objective for the one or more responses. Additionally, based on the user input, the computing device displays a subset of a set of multiple model types and a user-definable amount of design runs for the design. Each design run presents settings according to the design for each of the factors. The computing device also receives settings indicating a user-selected model type from the subset and a user-defined amount of design runs, and based on the settings, selects one or more design construction criteria for generating the design. The computing device then generates the design according to the design construction criteria.