1 DOE Resource Center
A DOE Terminology
B Types of Designed Experiments
C Tests of Significance
D Setting Up a Designed Experiment
E Plackett–Burman Matrices
F Taguchi Concepts
2 5.5.6. What are Taguchi designs?
3 Design of experiments via factorial designs
1 Introduction
2 What is Factorial Design?
2.1 Factorial Design Example
2.2 Null Outcome
2.3 Main Effects
2.4 Interaction Effects
3 Mathematical Analysis Approach
3.1 How to Deal with a 2n Factorial Design
3.2 Yates Algorithm
3.3 Factorial Design Example Revisited
4 Chemical Engineering Applications
5 Minitab DOE Example
5.1 Creating Factorial DOE
5.2 Modifying DOE Table
5.3 Analyzing DOE Results
5.4 Minitab Example for Centrifugal Contactor Analysis
6 Worked out Example 1
6.1 Solution to Example 1
7 Worked out Example 2
7.1 Solution to Example 2
8 Worked out Example 3
8.1 Solution to Example 3
9 Multiple Choice Question 1
10 Multiple Choice Question 2
11 Submitting answers to the multiple choice questions
12 Sage's Corner
13 References
4 Design of experiments via taguchi methods: orthogonal arrays
1 Introduction
2 Summary of Taguchi Method
2.1 Philosophy of the Taguchi Method
2.2 Taguchi Method Design of Experiments
3 Taguchi Loss Function
4 Determining Parameter Design Orthogonal Array
4.1 Important Notes Regarding Selection + Use of Orthogonal Arrays
5 Analyzing Experimental Data
6 Advantages and Disadvantages
7 Other Methods of Experimental Design
8 Worked out Example
9 Extreme Example: Sesame Seed Suffering
10 Multiple Choice Questions
10.1 Question 1
10.2 Question 2
11 Sage's Corner
12 References
5 ROLES & BENEFITS
A What can Design Of Experiments do for you?
B Benefits
C The Power of DOE
6 CONTRIBUTIONS OF DR. TAGUCHI
A DOE Simplification
B Parameter Design
C Signal-to-Noise (S/N) Ratios
D Dynamic Characteristics
E What is Quality?
F The Quality Loss Function (QLF)
7 INTRODUCTION TO TAGUCHI METHOD :
8 DESIGN OF EXPERIMENTS, QC AND TAGUCHI METHODS
9 DESIGN OF EXPERIMENTS
1 Introduction
2 Preparation
3 Components of Experimental Design
4 Purpose of Experimentation
5 Design Guidelines
6 Design Process
7 One Factor Experiments
8 Multi-factor Experiments (Downloadable Spreadsheets)
9 Taguchi Methods
10 Design of Experiments/Taguchi approach
11 Experimental Design (Industrial DOE)
DOE Overview
Experiments in Science and Industry
Differences in techniques
Overview
General Ideas
Computational Problems
Components of Variance, Denominator Synthesis
Summary
2**(k-p) Fractional Factorial Designs
Basic Idea
Generating the Design
The Concept of Design Resolution
Plackett-Burman (Hadamard Matrix) Designs for Screening
Enhancing Design Resolution via Foldover
Aliases of Interactions: Design Generators
Blocking
Replicating the Design
Adding Center Points
Analyzing the Results of a 2**(k-p) Experiment
Graph Options
Summary
2**(k-p) Maximally Unconfounded and Minimum Aberration Designs
Basic Idea
Design Criteria
Summary
3**(k-p) , Box-Behnken, and Mixed 2 and 3 Level Factorial Designs
Overview
Designing 3**(k-p) Experiments
An Example 3**(4-1) Design in 9 Blocks
Box-Behnken Designs
Analyzing the 3**(k-p) Design
ANOVA Parameter Estimates
Graphical Presentation of Results
Designs for Factors at 2 and 3 Levels
Central Composite and Non-Factorial Response Surface Designs
Overview
Design Considerations
Alpha for Rotatability and Orthogonality
Available Standard Designs
Analyzing Central Composite Designs
The Fitted Response Surface
Categorized Response Surfaces
Latin Square Designs
Overview
Latin Square Designs
Analyzing the Design
Very Large Designs, Random Effects, Unbalanced Nesting
Taguchi Methods: Robust Design Experiments
Overview
Quality and Loss Functions
Signal-to-Noise (S/N) Ratios
Orthogonal Arrays
Analyzing Designs
Accumulation Analysis
Summary
Mixture designs and triangular surfaces
Overview
Triangular Coordinates
Triangular Surfaces and Contours
The Canonical Form of Mixture Polynomials
Common Models for Mixture Data
Standard Designs for Mixture Experiments
Lower Constraints
Upper and Lower Constraints
Analyzing Mixture Experiments
Analysis of Variance
Parameter Estimates
Pseudo-Components
Graph Options
Designs for constrained surfaces and mixtures
Overview
Designs for Constrained Experimental Regions
Linear Constraints
The Piepel & Snee Algorithm
Choosing Points for the Experiment
Analyzing Designs for Constrained Surfaces and Mixtures
Constructing D- and A-optimal designs
Overview
Basic Ideas
Measuring Design Efficiency
Constructing Optimal Designs
General Recommendations
Avoiding Matrix Singularity
"Repairing" Designs
Constrained Experimental Regions and Optimal Design
Special Topics
Profiling Predicted Responses and Response Desirability
Residuals Analysis
Box-Cox Transformations of Dependent Variables
gonal arrays
gonal Arrays
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on Designs
al Designs
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Synopsis v
1 1
1
1.2 Necessity 2
1.3 Objective 3
1.4 Theme
2 4
4
4 6
4.1 Performance of System 6
5 CONCLUSIONS 7
5.1 Conclusions 7
5.2 Future Scope 8
Appendix – A
