Design Experiments Using Minitab Yanling Zuo( 左燕玲 ), PhD Minitab Inc.

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Presentation transcript:

Design Experiments Using Minitab Yanling Zuo( 左燕玲 ), PhD Minitab Inc.

MINITAB DOE Overview DOE menu Factorial

3 © Minitab Inc., 2003 MINITAB DOE Overview Response Surface → ً← Mixture

4 © Minitab Inc., 2003 MINITAB DOE Overview Taguchi

Case Study A quality team is studying how a catalytic reaction converts substrate into a final product. A sketch of the converter Feed 100% Reactants 70% products, 30% reactants catalyst Rev/min Temperature

Case Study… Factors identified after brainstorming: Feed rate – Flow rate settings for feed tank (10,15 ml/min) Catalyst (A, B) Agitation rate (100, 120) Temperature (140º, 180º C) Percent concentration (3%, 6%)

Case Study... Response: Percent of substrate reacted Data collection: The team has enough budget to perform 35 runs. They could run a full factorial design (2 5= 32). However, a better approach is to run a fractional design, analyze results, and decide on subsequent experimentation. What’s next? Create a ½ fraction design.

Case Study… Create the design with Minitab Go to Stat > DOE > Factorial > Create Factorial Design

Case Study… Output Note: Main effects confounded with 4-way interaction, 2-way interaction with 3-way interaction

Case Study… Worksheet

Case Study… Analyze the design with Minitab Go to Stat > DOE > Factorial > Analyze Factorial Design

Case Study… Normal Probability Plot of Effects

Case Study… Pareto chart of Effects

Case Study... Significant factors: Catalyst (B) Temp (D) Concentration (E) Catalyst x Temp (BD) Temp x Concentration (DE) What’s next: Remove non-significant effects and refit models.

Case Study... Output:

Case Study... Estimated coefficients: Reacted = – x Catalyst x Temp x Conc x Catelyst x Temp x Temp x Conc. (Can be used to predict percent reacted settings)

Case Study... Residual plots What’s next? Create factorial plots to find best settings.

Case Study... Factorial Plots

Case Study...

Conclusions: Feed rate and agitation do not have a significant impact Catalyst B, a temperature of 180ºC, and 3% concentration maximize substrate consumption. Followup experiment: The team had budget for 19 additional runs. They used Catalyst B and run a 2 2 full factorial design with 2 center points to detect curvature in the response. They centered experiment at currently known optimal settings,180ºC, 3%.

Case Study... Numerical output for the follow up experiment:

Case Study... Graphical output:

Case Study... Assessing Power: Design: 2 x 2, 1 replicate, 2 center points. Variance (MSE) = 1.28 St Dev = Size of effect: A change of 3% in reacted substrate.

Case Study... This design has low power (0.165).

Case Study... Conclusions: A quadratic effect on catalytic reaction due to temperature and concentration is present. This design has low power, not the best choice. A better design would include 2 replicates, but would require 12 runs (assuming 2 center points per replicate) rather than 6. Additional consideration: Consider using response surface methodology to model the curvature.