Design of Experiments (DOE) for Process and Product Optimization

This course is designed to help scientists and engineers plan and conduct experiments and analyze the data to develop predictive models used to optimize processes and products and solve complex problems. DOE is an extremely efficient method to understand which variables (and interactions) affect key outcomes and allows the development of mathematical models used to optimize process and product performance. The models also provide an understanding of the impact of variability in controllable and uncontrollable factors on important responses. The concepts behind DOE are covered along with some effective types of screening experiments. Case studies will also be presented to illustrate the use of the methods.

This highly interactive course will allow participants the opportunity to practice applying DOE techniques with various data sets. The objective is to provide participants with the key tools and knowledge to be able to apply the methods effectively in their process and product development efforts.

Learning Objectives:

• Plan and conduct experiments in an effective and efficient manner
• Apply good experimental practices when conducting studies
• Determine statistical significance of main and interaction effects
• Interpret significant main and interaction effects
• Develop predictive models to explain and optimize process/product behavior
• Check models for validity

Who will Benefit:

• Scientists
• Product and Process Engineers
• Design Engineers
• Quality Engineers
• Personnel involved in product development and validation
• Laboratory Personnel
• Manufacturing/Operations Personnel
• Process Improvement Personnel