Advanced DOE for Product Optimization and Robust Design Training

Developers and Six Sigma BBs often face situations where they have to identify optimum operating conditions or design layouts that are insensitive to a wide range of environmental and other noise factors. This course starts with an introduction to response surface methodology and then reaches out to cover modern, computer generated optimal designs, designs with randomization restrictions, and best practices to handle noise factors.

Course participants will learn to document previous knowledge and plan the experiment fro there, select and design an appropriate experimental approach, and run, analyze and interpret a Response Surface Experiment. During the course or in evening sessions participants will apply their knowledge in Glider or Lego Rover classroom case studies.

Course Content:

• Introduction to Design of Experiments (optional)

• Introduction to Response Surface Methodology (optional)

• DOE Planning, Preparation and Knowledge Management

• Advanced analysis of design quality, FDS

• Overview of of standard response surface designs: CCD, Box Behnken, Draper-Lin and other Minimum Run designs

• Selection of meaningful regression models

• Optimization of multiple response variables

• Dual response surface methods for robustness, analysis of propagation of error

• d-Optimal designs

• Dealing with constraints on experimental regions

• Dealing with constraints on randomization

This course is based on StatEase (R) Design Expert software.

Versions of this course are available for different statistical software packages including SAS(R) JMP(R) and CR Graph Visual XSel