Advanced Design of Experiments Course Description
This course explores how experimental design can be applied to the product and process optimization phase of development. Using Response Surface Methods, models that relate the process outputs to the process inputs are generated. These models allow one to determine optimal settings for the process and identify “sweet spots” that enable the process to be robust to uncontrollable variables.
During training participants will have an opportunity to use their newly acquired knowledge to actually design, run, and analyze a series of experiments via use of computer simulations for the closest thing to actually running industrial experiments.
Who Should Attend Advanced Design of Experiments
This course is designed for managers and engineers involved in product and process design (R&D) and improvement.
Through training, participants will:
- Gain an understanding of Response Surface Strategy for Process Optimization.
- Be able to fit Regression Models to relate the Response Variable(s) to the Critical Process Factors
- Be able to simultaneously optimize several Response Variables
Advanced Design of Experiments Course Outline
Introduction to Response Surface Methods
- Experimentation as an Iterative Process
- Goals and Strategy of RSM
- Case Study
First Order Models
- Experimental Designs for Fitting First Order Models
- Use of Center Points to Test Curvature
- Path of Steepest Ascent (POA)
Second Order Models
- Experimental Designs for Fitting Second Order Models: 3^k, Central Composite
- Designs, Box-Behnken Designs
- Rotatability and Orthogonality
- Blocking in RSM Designs
- Optimization of a Single Response Variable
Multiple Response Optimization
- Desirability Functions
- Optimization of a Several Response Variables
Basic DOE knowledge required
Advanced Design of Experiments Course Format
Combination lecture and classroom exercises
Available at QSG’s training facilities, on-site at your organization, and virtually