This webinar will cover several DOE topics including:
Experimentation is frequently performed using trial and error approaches which are extremely inefficient and rarely lead to optimal solutions. Furthermore, when it’s desired to understand the effect of multiple variables on an outcome (response), “one-factor-at-a-time” trials are often performed. Not only is this approach inefficient, it inhibits the ability to understand and model how multiple variables interact to jointly affect a response. Statistically based Design of Experiments provides a methodology for optimally developing process understanding via experimentation.
Design of Experiments has numerous applications, including:
This webinar will review the key concepts behind Design of Experiments. A strategy for utilizing sequential experiments to most efficiently understand and model a process is presented. Many common types of experiments and their applications are presented. These include experiments appropriate for screening, optimization, mixtures/formulations, etc. Several important techniques in experimental design (such as replication, blocking, and randomization) are introduced. A Case Study involving optimizing a manufacturing process with multiple responses is presented