Automated DOE Factor Analysis and Model Hierarchy Preservation
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Solution Overview
Problem
Current Design of Experiments (DOE) methods lack automation in analyzing data to determine the relationship between process factors and output, making it difficult to identify significant factors and preserve model hierarchy effectively.
Innovation Solution
A computer-based method that calculates the effects of process factors on the output, develops models including significant factors with estimated coefficients, and generates graphical representations to distinguish between significant and insignificant factors, using analysis of variance tests and model comparison statistics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If manual analysis methods are used to determine the relationship between process factors and output, then flexibility and interpretability are maintained, but automation and efficiency are reduced
Solution Approach 1:
The system performs self-service by automatically calculating factor effects, conducting ANOVA tests, and generating statistical models without requiring manual intervention. The computer system autonomously processes the experimental data, identifies significant factors, and preserves model hierarchy, thereby achieving high automation while managing complexity through algorithmic standardization.
2Measurement precision
If comprehensive statistical analysis is performed on all factors, then accuracy in identifying significant factors is improved, but computational time and resource usage increase
Solution Approach 1:
The system extracts and focuses only on the significant factors from the comprehensive set of process factors through automated ANOVA testing and statistical analysis. By calculating effect estimates and coefficients for all factors and then identifying only those that meet significance criteria, the system achieves high accuracy in factor identification while reducing the effective analysis time by concentrating results on the subset of significant factors rather than presenting all factors equally.
3Loss of information
If detailed graphical representations are generated for all factors, then completeness of information is improved, but clarity and ease of interpretation are reduced
Solution Approach 1:
The system applies local quality by providing different levels of graphical representation detail based on factor significance. Significant factors receive detailed graphical representations with estimated coefficients and effect estimates, while insignificant factors are either omitted or presented with minimal representation. This differentiated approach preserves complete information about all factors while enhancing clarity by emphasizing the most important factors through enhanced visual presentation.
Data Source
AI summary
A method of automatically analyzing data from at least one data set including a plurality of process factors of interest and a process output of interest to determine the relationship between the factors of interest and the output of interest at a given significance level and preserving model hierarchy. The method includes the steps of calculating the effects of the factors of interest against the process output of interest, developing a model including the significant factors of interest and respective estimated coefficients and omitting the insignificant factors of interest, generating a representation of the model, and generating at least one graphical representation of the factors of interest.