LEADER 05346nam 2200673 450 001 9910806224703321 005 20200520144314.0 010 $a0-08-099419-9 035 $a(CKB)3710000000089615 035 $a(EBL)1644387 035 $a(SSID)ssj0001156012 035 $a(PQKBManifestationID)11751047 035 $a(PQKBTitleCode)TC0001156012 035 $a(PQKBWorkID)11189276 035 $a(PQKB)10669223 035 $a(MiAaPQ)EBC4530240 035 $a(MiAaPQ)EBC1644387 035 $a(Au-PeEL)EBL4530240 035 $a(CaPaEBR)ebr11233279 035 $a(CaONFJC)MIL577690 035 $a(OCoLC)872990044 035 $a(PPN)225606852 035 $a(EXLCZ)993710000000089615 100 $a20160801h20142014 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aDesign of experiments for engineers and scientists /$fJiju Antony 205 $aSecond edition. 210 1$aAmsterdam, [Netherlands] :$cElsevier,$d2014. 210 4$dİ2014 215 $a1 online resource (221 p.) 225 1 $aElsevier Insights 300 $aDescription based upon print version of record. 311 $a0-08-099417-2 320 $aIncludes bibliographical references at the end of each chapters. 327 $aFront Cover; Design of Experiments for Engineers and Scientists; Copyright Page; Contents; Preface; Acknowledgements; 1 Introduction to Industrial Experimentation; 1.1 Introduction; 1.2 Some Fundamental and Practical Issues in Industrial Experimentation; 1.3 Statistical Thinking and its Role Within DOE; Exercises; References; 2 Fundamentals of Design of Experiments; 2.1 Introduction; 2.2 Basic Principles of DOE; 2.2.1 Randomisation; 2.2.2 Replication; 2.2.3 Blocking; 2.3 Degrees of Freedom; 2.4 Confounding; 2.4.1 Design Resolution 327 $a2.4.2 Metrology Considerations for Industrial Designed Experiments2.4.3 Measurement System Capability; 2.4.4 Some Tips for the Development of a Measurement System; 2.5 Selection of Quality Characteristics for Industrial Experiments; Exercises; References; 3 Understanding Key Interactions in Processes; 3.1 Introduction; 3.2 Alternative Method for Calculating the Two-Order Interaction Effect; 3.3 Synergistic Interaction Versus Antagonistic Interaction; 3.4 Scenario 1; 3.5 Scenario 2; 3.6 Scenario 3; Exercises; References; 4 A Systematic Methodology for Design of Experiments; 4.1 Introduction 327 $a4.2 Barriers in the Successful Application of DOE4.3 A Practical Methodology for DOE; 4.3.1 Planning Phase; Problem Recognition and Formulation; Selection of Response or Quality Characteristic; Selection of Process Variables or Design Parameters; Classification of Process Variables; Determining the Levels of Process Variables; List All the Interactions of Interest; 4.3.2 Designing Phase; 4.3.3 Conducting Phase; 4.3.4 Analysing Phase; 4.4 Analytical Tools of DOE; 4.4.1 Main Effects Plot; 4.4.2 Interactions Plots; 4.4.3 Cube Plots; 4.4.4 Pareto Plot of Factor Effects 327 $a4.4.5 NPP of Factor Effects4.4.6 NPP of Residuals; 4.4.7 Response Surface Plots and Regression Models; 4.5 Model Building for Predicting Response Function; 4.6 Confidence Interval for the Mean Response; 4.7 Statistical, Technical and Sociological Dimensions of DOE; 4.7.1 Statistical Dimension of DOE; 4.7.2 Technical Dimension of DOE; 4.7.3 Sociological and Managerial Dimensions of DOE; Exercises; References; 5 Screening Designs; 5.1 Introduction; 5.2 Geometric and Non-geometric P-B Designs; Exercises; References; 6 Full Factorial Designs; 6.1 Introduction 327 $a6.2 Example of a 22 Full Factorial Design6.2.1 Objective 1: Determination of Main/Interaction Effects That Influence Mean Plating Thickness; 6.2.2 Objective 2: Determination of Main/Interaction Effects That Influence Variability in Plating Thickness; 6.2.3 Objective 4: How to Achieve a Target Plating Thickness of 120 Units?; 6.3 Example of a 23 Full Factorial Design; 6.3.1 Objective 1: To Identify the Significant Main/Interaction Effects That Affect the Process Yield; 6.3.2 Objective 2: To Identify the Significant Main/Interaction Effects That Affect the Variability in Process Yield 327 $a6.3.3 Objective 3: What Is the Optimal Process Condition? 330 $aThe tools and techniques used in Design of Experiments (DoE) have been proven successful in meeting the challenge of continuous improvement in many manufacturing organisations over the last two decades. However research has shown that application of this powerful technique in many companies is limited due to a lack of statistical knowledge required for its effective implementation. Although many books have been written on this subject, they are mainly by statisticians, for statisticians and not appropriate for engineers. Design of Experiments for Engineers and Scientists