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Practice JMP using these webinar videos and resources. We hold live Mastering JMP Zoom webinars with Q&A most Fridays at 2 pm US Eastern Time. See the list and register. Local-language live Zoom webinars occur in the UK, Western Europe and Asia. See your country jmp.com/mastering site.
Created:
Oct 6, 2020 04:14 PM
| Last Modified: Oct 24, 2023 02:31 PM
See how to:
Understand the sequence of basic DOE capabilities (set up design, save/load factors and responses, simulate responses, select D- A- I- or Alias-Optimal criteria, ID number of starts, add interaction terms, modify number of JMP-recommended runs, run the design, make the design table)
Build a Classical Full Factorial design and model for confirmatory analysis
The goal: design the product to have lower fat and maintain adequate sheet strength
Create a full factorial design with two levels, no center points, no replicates
Build the model using the JMP DOE interface
Evaluate the Classical Full Factorial design
Determine if number of runs and factors are sufficient
Examine Power, where values closest to one indicates that if the displayed factors are active and important, then the probability is high that you will learn something about them when running the experiment and building the model
Examine Prediction Variance Profile, which shows midpoints of designs, and where low values are best
Examine Color Map Correlations, where value of 0 means factors are completely independent
Examine D-, G- and A- Efficiency, which compares your design to the best D-, G- or A-Optimal design, respectively
Examine the Prediction Variance for the Fraction of Design Space Plot, where low values are best
With the same goal of designing a product with lower fat and adequate sheet strength, build a Custom Full Factorial Design and modify it to include quadratic terms using Response Surface Methodology (RSM)
From the DOE interface, construct and evaluate a model based on Custom Full Factorial design
Examine Full Factorial pattern, which summarizes the values for each factor and where minus sign (-) indicates factor with low value and plus sign (+) indicates high value.
Examine Summary of Fit for each response to see if the mean explains the result better than the model
Remove terms that are >.05, unless it is a main effect related to a higher order term that is significant
Assure that the Residual by Predict Plot is random
Assure Probability>F is<.05, which indicates that the the model predicts better than the mean
Optimize factors using Prediction Profiler Desirability function to attempt to match targets for fat and sheet strength
Save values of optimization attempts using Maximize and Remember option
Simulate optimized model to try to find more experiments to run to help determine best settings
Use JMP Pro to compare Full Factorial and OFAT designs and determine Full Factorial at optimized settings is the best experiment to run to yield the required fat and sheet targets
Note: Q&A is included at times 27:18 and 50:06.
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