UsefulLinks
Statistics
Design of Experiments
1. Introduction to Design of Experiments
2. Fundamental Principles of Experimental Design
3. Planning and Conducting Experiments
4. Simple Comparative Experiments
5. Analysis of Variance (ANOVA)
6. Factorial Designs
7. Fractional Factorial Designs
8. Response Surface Methodology
9. Robust Design and Taguchi Methods
10. Advanced and Specialized Designs
11. Statistical Analysis and Model Building
12. Software Tools and Implementation
13. Quality Control and Best Practices
14. Applications and Case Studies
9.
Robust Design and Taguchi Methods
9.1.
Robustness Concepts
9.1.1.
Definition of Robust Design
9.1.2.
Sources of Variation
9.1.3.
Quality Engineering Philosophy
9.2.
Control and Noise Factors
9.2.1.
Factor Classification
9.2.2.
Noise Factor Types
9.2.2.1.
External Noise
9.2.2.2.
Internal Noise
9.2.2.3.
Unit-to-Unit Noise
9.2.3.
Control Factor Selection
9.2.4.
Noise Factor Management
9.3.
Taguchi's Quality Philosophy
9.3.1.
Quality Loss Function
9.3.1.1.
Nominal-the-Best
9.3.1.2.
Smaller-the-Better
9.3.1.3.
Larger-the-Better
9.3.2.
Off-line Quality Control
9.3.3.
Parameter vs. Tolerance Design
9.4.
Signal-to-Noise Ratios
9.4.1.
S/N Ratio Concepts
9.4.2.
S/N Ratio Types and Calculations
9.4.3.
Interpretation Guidelines
9.4.4.
Limitations and Criticisms
9.5.
Orthogonal Arrays in Robust Design
9.5.1.
Array Selection
9.5.2.
Factor Assignment
9.5.3.
Interaction Considerations
9.5.4.
Analysis Procedures
9.6.
Parameter Design Experiments
9.6.1.
Inner-Outer Array Structure
9.6.2.
Crossed Array Designs
9.6.3.
Combined Array Approaches
9.6.4.
Analysis and Optimization
9.7.
Alternative Approaches to Robust Design
9.7.1.
Response Surface Methods
9.7.2.
Dual Response Approach
9.7.3.
Robust Parameter Design
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8. Response Surface Methodology
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10. Advanced and Specialized Designs