Optimal design of experiments : a case study approach / / Peter Goos
(Visualizza in formato marc)
(Visualizza in BIBFRAME)
| Autore: |
Goos Peter
|
| Titolo: |
Optimal design of experiments : a case study approach / / Peter Goos
|
| Pubblicazione: | Chicester : , : Wiley, , 2011 |
| Descrizione fisica: | 1 online resource (xiv, 287 pages) : illustrations, charts |
| Disciplina: | 500 |
| 620.00420285 | |
| Soggetto topico: | Industrial engineering - Experiments - Computer-aided design |
| Experimental design - Data processing | |
| Industrial engineering | |
| Computer-aided design | |
| Soggetto genere / forma: | Case studies. |
| Classificazione: | SCI028000 |
| Persona (resp. second.): | JonesBradley |
| Nota di bibliografia: | lncludes bibliographical references (pages 277-282) and index. |
| Nota di contenuto: | 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. |
| Sommario/riassunto: | "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"-- |
| Altri titoli varianti: | Case study approach |
| Titolo autorizzato: | Optimal design of experiments |
| ISBN: | 1-283-17783-8 |
| 9786613177834 | |
| 1-119-97401-1 | |
| 1-119-97400-3 | |
| Formato: | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione: | Inglese |
| Record Nr.: | 9911019143203321 |
| Lo trovi qui: | Univ. Federico II |
| Opac: | Controlla la disponibilità qui |