This book demonstrates the utility of the computer-aided optimal design approach using real industrial examples. These examples address questions such as the following:
- How can I do screening inexpensively if I have dozens of factors to investigate?
- What can I do if I have day-to-day variability and I can only perform 3 runs a day?
- How can I do RSM cost effectively if I have categorical factors?
- How can I design and analyze experiments when there is a factor that can only be changed a few times over the study?
- How can I include both ingredients in a mixture and processing factors in the same study?
- How can I design an experiment if there are many factor combinations that are impossible to run?
- How can I make sure that a time trend due to warming up of equipment does not affect the conclusions from a study?
- How can I take into account batch information in when designing experiments involving multiple batches?
- How can I add runs to a botched experiment to resolve ambiguities?
While answering these questions the book also shows how to evaluate and compare designs. This allows researchers to make sensible trade-offs between the cost of experimentation and the amount of information they obtain.
Keywords: Experimental Design, computer-aided optimal design approach, how to do RSM cost effectively, explaining the concept of tailored DOE, basics of optimal design, optimal design using flow, optimization or engineering interpretation