Most processes respond to changes in process operating conditions (temperature, humidity, etc.), material formulation, machining speeds, catalytic intensity, gestation time, and the like. Design of Experiment is a systematic way of evaluating:
- Which factors have a significant effect on process quality measurements and which do not;
- Which factors operate independently of others and which interact in their effects, and
- What should the optimal settings be on the factors that are controllable by the operator,and/or process and design engineers.
The objective of designed experiments is to answer these questions while extracting the maximum amount of usable information with the fewest number of observations.
A Design of Experiment (DOE) is a structured method for determining the relationship between factors affecting a process and the output of that process. This course focuses on the scientific and statistical methods involved in planning and analyzing DOEs in order to yield practical results. The course covers the methodology of design of experiments; discuss how to plan a design of experiment; explain the importance of each concept used in design of experiments; discuss variables in an experiment and how they interact; interpret analysis of variance (ANOVA); explain how to conduct and analyze the results of a contrast test; discuss how to discover hidden interactions; identify the advantages, disadvantages, assumptions and hypotheses related to various types of designs, including completely randomized design, completely randomized block design, and factorial designs. The course also covers the practical aspects of DOE.
Upon completion of this course participants will have learned the critical tools and requirements to solving complex quality problems, reducing product and process variation, and optimizing product/process performance and consistency. Some understanding of basic statistical methods will be needed to complete this course.