03617nam 2200577 450 991082673890332120230803195431.03-95489-709-1(CKB)2670000000534327(EBL)1640319(SSID)ssj0001217053(PQKBManifestationID)11680956(PQKBTitleCode)TC0001217053(PQKBWorkID)11201970(PQKB)10874961(MiAaPQ)EBC1640319(Au-PeEL)EBL1640319(CaPaEBR)ebr10856450(OCoLC)871859414(EXLCZ)99267000000053432720140421h20142014 uy 0engur|n|---|||||txtccrRsm a key to optimize machining : multi-response optimization of CNC turning with Al-7020 alloy /Bikram Jit Singh, Harsimran Singh SodhiHamburg, Germany :Anchor Academic Publishing,2014.℗20141 online resource (118 p.)Description based upon print version of record.3-95489-209-X Includes bibliographical references.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 METHODOLOGY4.1 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 Between ResponsesCHAPTER 8: CONCLUSIONS & SCOPE8.1 Conclusion; 8.2 Scope for Future; REFERENCES; WEB-SOURCES; BIOGRAPHICAL NOTESParametric 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)Experimental designGraphic methodsResponse surfaces (Statistics)GermanyExperimental designGraphic methods.Response surfaces (Statistics)001.434Singh Bikram Jit1621120Sodhi Harsimran SinghMiAaPQMiAaPQMiAaPQBOOK9910826738903321Rsm3954265UNINA