Response Surface Experimental Design for Missing Target Values
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Solution Overview
Problem
In complex control processes, setting efficient and accurate experimental designs for multiple control parameters is challenging, especially when missing data points occur, leading to increased experimental efforts and inefficiencies.
Innovation Solution
An information processing method that creates a first table of experimental conditions using level values, calculates a response surface, and adjusts the level values by adding a fourth level value different from the initial values if the response surface does not include a target value, thereby refining the experimental design.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the number of control parameters increases to handle complex control processes, then the comprehensiveness of control is improved, but the number of experimental candidate points increases rapidly leading to increased experimental effort and time consumption
Solution Approach 1:
The patent segments the experimental design process into two phases: first creating a base experimental design without the problematic control parameter, then separately determining the levels for the additional control parameter based on response surface analysis. This segmentation reduces the combinatorial explosion of experimental points while maintaining comprehensive control coverage.
Solution Approach 2:
The patent treats the additional control parameter as a separate dimension that is determined after establishing the base experimental design. By using response surface analysis to guide the selection of levels for the additional parameter, the method adds dimensional coverage without proportionally increasing the number of experimental points in the base design space.
2Measurement precision
If additional experimental points are selected to compensate for missing data points, then the accuracy of characteristic models is improved, but the complexity of selecting optimal combinations from candidate points increases significantly
Solution Approach 1:
The patent performs preliminary action by first creating a complete base experimental design that covers all control parameters except the additional one. This base design is established before considering the additional parameter, allowing systematic determination of its levels based on response surface analysis rather than dealing with complex combinatorial selection from the outset.
Solution Approach 2:
The patent uses response surface analysis as a feedback mechanism to guide the determination of levels for the additional control parameter. The response surface, calculated from the base experimental design, provides feedback information about optimal parameter settings, enabling accurate model construction without exhaustive combinatorial search.
Data Source
AI summary
An information processing method includes: creating a first table by an experimental design method, calculating a first response surface using the first table, setting a fourth level value for a first control factor when the first response surface does not include a target value related to an object variable for the first control factor, creating a second table by the experimental design method by deleting at least one combination of the experimental conditions which include one level value for the first control factor from the first table and adding at least one combination of the experimental conditions based on the plurality of level values including the fourth level value and without including the one level value for the first control factor, calculating a second response surface including the target value using the second table, and outputting the second response surface.