Success is no accident with design of experiments (DOE). A strategically planned and executed experiment gives you a great deal of information about the effect on a response variable due to one or more factors. Design of Experiments (DOE) is a methodology that can be effective for general problem-solving, as well as for improving or optimizing product design and manufacturing processes. Specific applications of DOE include identifying proper design dimensions and tolerances, achieving robust designs, generating predictive math models that describe physical system behavior, and determining ideal manufacturing settings. After this course, you’ll be able to identify what factors impact quality and what you can do to improve.
Learning Objectives:
- Build a model and check that model.
- Apply the foundation skills necessary to move on to more complex, multilevel designs.
- Understand experimental analysis: main and interactive effects, experimental error, normal probability plots, identification of “active” efforts, and residual analysis.
- Identify the variables that have the greatest impact on product-level quality.
- Understand experimental design essentials, be able to plan an experiment (choose factors, levels, design matrices), and set up, conduct, and analyze a two-level factorial experiment.
- Apply the fundamentals of designed experiments, including comparative experiments, process optimization, and multiple variable designs to continuously improve all product stages.
- Know how to use simple graphical techniques to analyze data.
