Design and Analysis of Experiments

The training can be made up of any of the following topics, the number of topics included in the course will depend on the length of the training and level required.

Basic Concepts

• Replication v Repeat Testing
  
• Randomisation
  
• Blocking to control noise variation
  
• Planning an experimental program
  
• Benefits and limitations of the approach
  

Factorial Designs

• Importance of detecting and understanding interactions
  
• Visualisation of effects
  
• Fractional factorials - benefits and pitfalls
  

Response Surface Designs

• Factorial and Central Composite Designs
  
• Simple response modelling
  
• Contour plots for model visualisation
  
• Optimisation
  
• Assessing fir of models
  
• D-Optimal designs
  

Nested Designs

• Estimation of variance components
  
• Applications to Guage R&R assessment
  

Orthogonal Arrays

• Highly fractional factorial designs for efficient estimation of many effects
  
• Benefits and dangers
  
• The Taguchi approach to process optimisation
  

Mixture Designs

• Designing experiments with constraints on the factor space
  
• D-Optimal designs