04111nam 22006493u 450 991101914320332120260113153019.01-283-17783-897866131778341-119-97401-11-119-97400-3(CKB)2550000000041193(EBL)697607(OCoLC)747411905(SSID)ssj0000539756(PQKBManifestationID)11327619(PQKBTitleCode)TC0000539756(PQKBWorkID)10580065(PQKB)10298968(PPN)262117177(EXLCZ)99255000000004119320130418d2011 uy 0engur|n|---|||||txtccrOptimal design of experiments a case study approach /Peter GoosChicester :Wiley,2011.1 online resource (xiv, 287 pages) illustrations, charts0-470-74461-8 Print version: Goos, Peter. Optimal design of experiments. Hoboken, N.J. : Wiley, 2011 (DLC) 2011008381 lncludes bibliographical references (pages 277-282) and index.A Simple Comparative Experiment -- An Optimal Screening Experiment -- Adding Runs to a Screening Experiment -- A Response Surface Design with a Categorical Factor -- A Response Surface Design in an Irregularly Shaped Design Region -- A 'Mixture' Experiment with Process Variables -- A Response Surface Design in Blocks -- A Screening Experiment in Blocks -- Experimental Design in the Presence of Covariates -- A Split-Plot Design -- A Two-Way Split-Plot Design."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"--Provided by publisher.Case study approachIndustrial engineeringExperimentsComputer-aided designCase studiesExperimental designData processingIndustrial engineeringComputer-aided designCase studies.lcgftIndustrial engineeringExperimentsComputer-aided designExperimental designData processing.Industrial engineering.Computer-aided design.500620.00420285SCI028000bisacshGoos Peter1598894Jones BradleyAU-PeELAU-PeELAU-PeELBOOK9911019143203321Optimal design of experiments4527342UNINA