Factorial experiments are often used in factor screening.; that is, identify the subset of factors in a process or system that are of primary important to the response. Once the set of important factors are identified interest then usually turns to optimization; that is, what levels of the important factors produce the best values of the response. This course provides design and optimization tools to answer that questions using the response surface framework. Other related topics include design and analysis of computer experiments, experiments with mixtures, and experimental strategies to reduce the effect of uncontrollable factors on unwanted variability in the response.
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Conduct experiments w/computer models and understand how least squares regression is used to build an empirical model from experimental design data
Understand the response surface methodology strategy to conduct experiments where system optimization is the objective
Recognize how the response surface approach can be used for experiments where the factors are the components of a mixture
Recognize where the objective of the experiment is to minimize the variability transmitted into the response from uncontrollable factors
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Programme de cours : ce que vous apprendrez dans ce cours
Unit 1: Additional Design and Analysis Topics for Factorial and Fractional Factorial Designs
Unit 2: Regression Models
Unit 3: Response Surface Methods and Designs
Unit 4: Robust Parameter Design and Process Robustness Studies
Avis
- 5 stars80 %
- 4 stars8 %
- 3 stars10 %
- 2 stars2 %
Meilleurs avis pour RESPONSE SURFACES, MIXTURES, AND MODEL BUILDING
It was a great experience for me to do the RSM model building an online course. I learned experimental designs for fitting response surfaces.
DoE is an essential but forgotten initial step in the experimental work! This course gives a very good start and breaking the ice for higher quality of experimental work.
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