Optimal Design of Experiments: A Case Study Approach

"This book demonstrates the utility of the computer-aided optimal design approach using real industrial examples"--
"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 thestudy? 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. The structure of the book is organized around the following chapters: 1) Introduction explaining the concept of tailored DOE. 2) Basics of optimal design. 3) Nine case studies dealing with the above questions using the flow: description → design → analysis → optimization or engineering interpretation. 4)Summary. 5) Technical appendices for the mathematically curious"--
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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. The structure of the book is organized around the following chapters: 1) Introduction explaining the concept of tailored DOE. 2) Basics of optimal design. 3) Nine case studies dealing with the above questions using the flow:• description → design → analysis → optimization or engineering interpretation. 4) Summary.5) Technical appendices for the mathematically curious.