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|a 001.434
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|a UAMI
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|a Singh, Bikram Jit,
|e author.
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|a Rsm :
|b a key to optimize machining : multi-response optimization of CNC turning with Al-7020 alloy /
|c Bikram Jit Singh, Harsimran Singh Sodhi.
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|a Hamburg, Germany :
|b Anchor Academic Publishing,
|c 2014.
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|c ©2014
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|a 1 online resource (118 pages) :
|b illustrations, tables, photographs
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|a text
|b txt
|2 rdacontent
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|a computer
|b c
|2 rdamedia
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|a online resource
|b cr
|2 rdacarrier
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|a Includes bibliographical references.
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|a Online resource; title from PDF title page (ebrary, viewed April 19, 2014).
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|a RSM: A Key to Optimize Machining; ACKNOWLEDGEMENTS; PREFACE; Contents; List of figures; List of Tables; CHAPTER 1: MACHINING AND CNC MACHINING; 1.1 Machining: An Introduction; 1.2 Machining Operations; 1.3 An Overview of Machining Technology; 1.4 CNC Lathe / CNC Turning Center; 1.5 Present Work; 1.6 Machining Parameters; 1.7 Summary; CHAPTER 2: CUTTING TOOLS; 2.1 Tools; 2.2 Multiple Cutting-Edge Tools; 2.3 Stages in Metal Cutting; 2.4 Tool Material; 2.5 Tool Wear; CHAPTER 3: ALUMINIUM AND ITS ALLOYS; 3.1 Aluminium; 3.2 Fundamentals of Aluminum Alloys; CHAPTER 4: RESPONSE SUFRFACE METHODOLOGY.
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|a 4.1 RSM4.2 Outline of ANOVA; 4.3 Considered Responses; 4.4 Motivation of Study; CHAPTER 5: BACKGROUND OF MACHINING OPTIMIZATION; CHAPTER 6: MACHINING OF ALUMINIUM AND ITS ALLOYS; 6.1 CNC Machining; 6.2 Methodology Proposed; CHAPTER 7: MACHINING OPTIMIZATION: A CASE STUDY; 7.1 Machining Parameters along with their Levels; 7.2. RSM Matrix; 7.3. Execution of Designed Experiments; 7.4. RSM Statistics for MRR; 7.5 Graphical Inferences for MRR; 7.6 RSM Statistics for Ra; 7.7 Inferences for Ra; 7.8 RSM Solution; 7.9. Validation of Solution through ANOVA; 7.10 Relation In Betwen Responses.
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|a CHAPTER 8: CONCLUSIONS & SCOPE8.1 Conclusion; 8.2 Scope for Future; REFERENCES; WEB-SOURCES; BIOGRAPHICAL NOTES.
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|a Parametric optimization, especially in machining of non-ferrous alloys seems to be quite rare and needs an immediate attention because of its associated downstream financial and non-financial losses. This book tries to fill the gap and presents an optimization problem of commonly used Al-7020 Alloy. Principles of Response Surface Methodology (RSM) have been implemented through Minitab software to bring necessary multi-response optimization, while turning on a CNC turner. The present study focuses on to enhance Material Removal Rate (MRR) while simultaneously reducing the Surface Roughness (Ra).
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|a ProQuest Ebook Central
|b Ebook Central Academic Complete
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|a Experimental design
|x Graphic methods.
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|a Response surfaces (Statistics)
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|a Plan d'expérience
|x Méthodes graphiques.
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|a Surfaces de réponse (Statistique)
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|a REFERENCE
|x Questions & Answers.
|2 bisacsh
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|a Response surfaces (Statistics)
|2 fast
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|a Sodhi, Harsimran Singh,
|e author.
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|i has work:
|a RSM : A Key to Optimize Machining (Text)
|1 https://id.oclc.org/worldcat/entity/E39PCYDdpKmvpGttMjWWBdYfpX
|4 https://id.oclc.org/worldcat/ontology/hasWork
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0 |
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|i Print version:
|a Singh, Bikram Jit.
|t Rsm : a key to optimize machining : multi-response optimization of CNC turning with Al-7020 alloy.
|d Hamburg, Germany : Anchor Academic Publishing, ©2014
|h 119 pages
|z 9783954892099
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856 |
4 |
0 |
|u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=1640319
|z Texto completo
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