Optimal design of experiments: a case study approach
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Hoboken, N.J.
Wiley
2011
|
| Schlagworte: | |
| Beschreibung: | Includes bibliographical references and index "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"-- "This book demonstrates the utility of the computer-aided optimal design approach using real industrial examples"-- |
| Beschreibung: | xiv, 287 p. |
| ISBN: | 9781119974017 9781119976165 9781119976172 9780470744611 9781119974000 |
Internformat
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| 100 | 1 | |a Goos, Peter |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Optimal design of experiments |b a case study approach |c Peter Goos, Bradley Jones |
| 264 | 1 | |a Hoboken, N.J. |b Wiley |c 2011 | |
| 300 | |a xiv, 287 p. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b c |2 rdamedia | ||
| 338 | |b cr |2 rdacarrier | ||
| 500 | |a Includes bibliographical references and index | ||
| 500 | |a "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"-- | ||
| 500 | |a "This book demonstrates the utility of the computer-aided optimal design approach using real industrial examples"-- | ||
| 650 | 4 | |a Datenverarbeitung | |
| 650 | 4 | |a Industrial engineering |x Experiments |x Computer-aided design | |
| 650 | 4 | |a Experimental design |x Data processing | |
| 650 | 4 | |a Industrial engineering |v Case studies | |
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Datensatz im Suchindex
| _version_ | 1804177260598525952 |
|---|---|
| any_adam_object | |
| author | Goos, Peter |
| author_facet | Goos, Peter |
| author_role | aut |
| author_sort | Goos, Peter |
| author_variant | p g pg |
| building | Verbundindex |
| bvnumber | BV044154604 |
| collection | ZDB-30-PAD |
| ctrlnum | (ZDB-30-PAD)EBC697607 (ZDB-89-EBL)EBL697607 (OCoLC)747411905 (DE-599)BVBBV044154604 |
| dewey-full | 670.285 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 670 - Manufacturing |
| dewey-raw | 670.285 |
| dewey-search | 670.285 |
| dewey-sort | 3670.285 |
| dewey-tens | 670 - Manufacturing |
| discipline | Werkstoffwissenschaften / Fertigungstechnik |
| format | Electronic eBook |
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| id | DE-604.BV044154604 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T07:45:15Z |
| institution | BVB |
| isbn | 9781119974017 9781119976165 9781119976172 9780470744611 9781119974000 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-029561449 |
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| open_access_boolean | |
| physical | xiv, 287 p. |
| psigel | ZDB-30-PAD |
| publishDate | 2011 |
| publishDateSearch | 2011 |
| publishDateSort | 2011 |
| publisher | Wiley |
| record_format | marc |
| spelling | Goos, Peter Verfasser aut Optimal design of experiments a case study approach Peter Goos, Bradley Jones Hoboken, N.J. Wiley 2011 xiv, 287 p. txt rdacontent c rdamedia cr rdacarrier Includes bibliographical references and index "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"-- "This book demonstrates the utility of the computer-aided optimal design approach using real industrial examples"-- Datenverarbeitung Industrial engineering Experiments Computer-aided design Experimental design Data processing Industrial engineering Case studies Statistik (DE-588)4056995-0 gnd rswk-swf Optimale Versuchsplanung (DE-588)4043660-3 gnd rswk-swf Lineares Modell (DE-588)4134827-8 gnd rswk-swf (DE-588)4522595-3 Fallstudiensammlung gnd-content Optimale Versuchsplanung (DE-588)4043660-3 s Statistik (DE-588)4056995-0 s 1\p DE-604 Lineares Modell (DE-588)4134827-8 s 2\p DE-604 Jones, Bradley Sonstige oth 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 2\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Goos, Peter Optimal design of experiments a case study approach Datenverarbeitung Industrial engineering Experiments Computer-aided design Experimental design Data processing Industrial engineering Case studies Statistik (DE-588)4056995-0 gnd Optimale Versuchsplanung (DE-588)4043660-3 gnd Lineares Modell (DE-588)4134827-8 gnd |
| subject_GND | (DE-588)4056995-0 (DE-588)4043660-3 (DE-588)4134827-8 (DE-588)4522595-3 |
| title | Optimal design of experiments a case study approach |
| title_auth | Optimal design of experiments a case study approach |
| title_exact_search | Optimal design of experiments a case study approach |
| title_full | Optimal design of experiments a case study approach Peter Goos, Bradley Jones |
| title_fullStr | Optimal design of experiments a case study approach Peter Goos, Bradley Jones |
| title_full_unstemmed | Optimal design of experiments a case study approach Peter Goos, Bradley Jones |
| title_short | Optimal design of experiments |
| title_sort | optimal design of experiments a case study approach |
| title_sub | a case study approach |
| topic | Datenverarbeitung Industrial engineering Experiments Computer-aided design Experimental design Data processing Industrial engineering Case studies Statistik (DE-588)4056995-0 gnd Optimale Versuchsplanung (DE-588)4043660-3 gnd Lineares Modell (DE-588)4134827-8 gnd |
| topic_facet | Datenverarbeitung Industrial engineering Experiments Computer-aided design Experimental design Data processing Industrial engineering Case studies Statistik Optimale Versuchsplanung Lineares Modell Fallstudiensammlung |
| work_keys_str_mv | AT goospeter optimaldesignofexperimentsacasestudyapproach AT jonesbradley optimaldesignofexperimentsacasestudyapproach |