《Introduction to statistical quality control Third Edition》求取 ⇩

CHAPTER 1QUALITY IMPROVEMENT IN THE MODERN BUSINESS ENVIRONMENT1

1-1The Meaning of Quality and Quality Improvement2

1-1.1 Dimensions of Quality2

1-1.2 Quality Engineering Terminology6

1-2 A Brief History of Quality Methodology8

1-3 Statistical Methods for Quality Improvement12

1-4Total Quality Management17

1-4.1 Quality Philosophy17

1-4.2 The Link Between Quality and Productivity21

1-4.3 Quality Costs22

1-4.4 Legal Aspects of Quality28

1-4.5 Implementing Quality Improvement30

PART ⅠSTATISTICAL METHODS USEFUL IN QUALITY IMPROVEMENT33

CHAPTER 2MODELING PROCESS QUALITY34

2-1Describing Variation35

2-1.1 The Stem and Leaf Plot35

2-1.2 The Frequency Distribution and Histogram38

2-1.3 Numerical Summary of Data40

2-1.4 The Box Plot43

2-1.5 Sample Computer Output44

2-1.6 Probability Distributions46

2-2Important Discrete Distributions51

2-2.1 The Hypergeometric Distribution51

2-2.2 The Binomial Distribution52

2-2.3 The Poisson Distribution55

2-2.4 The Pascal and Related Distributions56

2-3Important Continuous Distributions57

2-3.1 The Normal Distribution57

2-3.2 The Exponential Distribution62

2-3.3 The Gamma Distribution65

2-3.4 The Weibull Distribution67

2-4Some Useful Approximations69

2-4.1 The Binomial Approximation to the Hypergeometric69

2-4.2 The Poisson Approximation to the Binomial69

2-4.3 The Normal Approximation to the Binomial70

2-4.4 Comments on Approximations70

2-5 Exercises71

CHAPTER 3INFERENCES ABOUT PROCESS QUALITY77

3-1Statistics and Sampling Distributions78

3-1.1 Sampling from a Normal Distribution79

3-1.2 Sampling from a Bernoulli Distribution83

3-1.3 Sampling from a Poisson Distribution84

3-2Estimation of Process Parameters85

3-2.1 Point Estimation85

3-2.2 Interval Estimation86

3-3Hypothesis Testing on Process Parameters96

3-3.1 Tests on Means, Variance Known97

3-3.2 The Use of P-Values in Hypothesis Testing100

3-3.3 Tests on Means of Normal Distributions, Variance Unknown101

3-3.4 Tests on Variances of Normal Distributions107

3-3.5 Tests on Binomial Parameters109

3-3.6 Tests on Poisson Parameters110

3-3.7 Probability Plotting113

3-3.8 The Probability of Type Ⅱ Error116

3-4 Exercises119

PART ⅡSTATISTICAL PROCESS CONTROL127

CHAPTER 4METHODS AND PHILOSOPHY OF STATISTICAL PROCESS CONTROL129

4-1 Introduction130

4-2 Chance and Assignable Causes of Quality Variation130

4-3Statistical Basis of the Control Chart132

4-3.1 Basic Principles132

4-3.2 Choice of Control Limits138

4-3.3 Sample Size and Sampling Frequency140

4-3.4 Rational Subgroups143

4-3.5 Analysis of Patterns on Control Charts146

4-3.6 Discussion of Sensitizing Rules for Control Charts149

4-4 The Rest of the “Magnificent Seven”150

4-5 Implementing SPC158

4-6 An Application of SPC159

4-7 Nonmanufacturing Applications of Statistical Process Control167

4-8 Exercises174

CHAPTER 5CONTROL CHARTS FOR VARIABLES179

5-1 Introduction180

5-2Control Charts for x and R181

5-2.1 Statistical Basis of the Charts181

5-2.2 Development and Use of x and R Charts186

5-2.3 Charts Based onStandard Values201

5-2.4 Interpretation of x and R Charts202

5-2.5 The Effect of Nonnormality on x and R Charts205

5-2.6 The Operating-Characteristic Function206

5-2.7 The Average Run Length for the x Chart209

5-3 Control Charts for x and S211

5-3.1Construction and Operation of x and S Charts212

5-3.2 The x and S Control Charts with Variable Sample Size217

5-3.3 The S2 Control Chart221

5-4 Control Charts for Individual Measurements221

5-5 Summary of Procedures for x, R, and S Charts229

5-6 Applications of Variables Control Charts230

5-7 Exercises235

CHAPTER 6CONTROL CHARTS FOR ATTRIBUTES250

6-1 Introduction251

6-2The Control Chart for Fraction Nonconforming251

6-2.1 Development and Operation of the Control Chart253

6-2.2 Variable Sample Size265

6-2.3 Nonmanufacturing Applications270

6-2.4 The Operating-Characteristic Function and Average Run Length Calculations271

6-3 Control Charts for Nonconformities （Defects）275

6-3.1Procedures with Constant Sample Size275

6-3.2 Procedures with Variable Sample Size285

6-3.3 Demerit Systems287

6-3.4 The Operating-Characteristic Function289

6-3.5 Dealing with Low Defect Levels290

6-3.6 Nonmanufacturing Applications294

6-4 Choice Between Attributes and Variables Control Charts294

6-5 Guidelines for Implementing Control Charts299

6-6 Exercises304

CHAPTER 7CUMULATIVE SUM AND EXPONENTIALLY WEIGHTED MOVING AVERAGE CONTROL CHARTS313

7-1The Cumulative-Sum Control Chart314

7-1.1 Basic Principles: The Cusum Control Chart for Monitoring the Process Mean314

7-1.2 The Tabular or Algorithmic Cusum for Monitoring the Process Mean317

7-1.3 Recommendations for Cusum Design322

7-1.4 The Standardized Cusum324

7-1.5 Rational Subgroups325

7-1.6 Improving Cusum Responsiveness for Large Shifts325

7-1.7 The Fast Initial Response or Headstart Feature325

7-1.8 One-Sided Cusums327

7-1.9 A Cusum for Monitoring Process Variability328

7-1.10 Cusums for Other Sample Statistics329

7-1.11 The V-Mask Procedure329

7-2The Exponentially Weighted Moving-Average Control Chart332

7-2.1 The Exponentially Weighted Moving-Average Control Chart for Monitoring the Process Mean333

7-2.2 Design of an EWMA Control Chart337

7-2.3 Rational Subgroups339

7-2.4 Extensions of the EWMA339

7-3 The Moving Average Control Chart341

7-4 Exercises344

CHAPTER 8OTHER STATISTICAL PROCESS CONTROL TECHNIQUES348

8-1Statistical Process Control for Short Production Runs349

8-1.1 x and R Charts for Short Production Runs349

8-1.2 Attribute Control Charts for Short Production Runs352

8-1.3 Other Methods353

8-2Modified and Acceptance Control Charts354

8-2.1 Modified Control Limits for the x Chart354

8-2.2 Acceptance Control Charts357

8-3 Group Control Charts for Multiple-Stream Processes358

8-4Multivariate Quality Control360

8-4.1 Monitoring of Means362

8-4.2 Monitoring Process Variability372

8-5 SPC with Correlated Data374

8-6Interfacing Statistical Process Control and Engineering Process Control386

8-6.1 Process Monitoring and Process Regulation386

8-6.2 Combining SPC and EPC395

8-7Economic Design of Control Charts399

8-7.1 Designing a Control Chart399

8-7.2 Process Characteristics399

8-7.3 Cost Parameters400

8-7.4 Early Work and Semi-Economic Design402

8-7.5 An Economic Model of the x Control Chart403

8-7.6 Other Work412

8-8Overview of Other Procedures413

8-8.1 Tool Wear413

8-8.2 Control Charts Based on Other Sample Statistics414

8-8.3 Adaptive Schemes415

8-8.4 Selecting the Optimum Target Value for a Process417

8-8.5 Fill Control419

8-8.6 Precontrol419

8-9 Exercises421

CHAPTER 9PROCESS CAPABILITY ANALYSIS430

9-1 Introduction431

9-2Process-Capability Analysis Using a Histogram or a Probability Plot433

9-2.1 Using the Histogram433

9-2.2 Probability Plotting434

9-3Process Capability Ratios438

9-3.1 Use and Interpretation of PCR438

9-3.2 Process-Capability Ratio for an Off-Center Process442

9-3.3 Normality and the Process Capability Ratio444

9-3.4 More About Process Centering444

9-3.5 Confidence Intervals and Tests on Process Capability Ratios447

9-4 Process-Capability Analysis Using a Control Chart451

9-5 Process-Capability Analysis Using Designed Experiments453

9-6 Gage and Measurement System Capability Studies455

9-7Setting Specification Limits on Discrete Components461

9-7.1 Linear Combinations461

9-7.2 Nonlinear Combinations465

9-8Estimating the Natural Tolerance Limits of a Process467

9-8.1 Tolerance Limits Based on the Normal Distribution468

9-8.2 Nonparametric Tolerance Limits469

9-9 Exercises470

PART ⅢPROCESS DESIGN AND IMPROVEMENT WITH DESIGNED EXPERIMENTS475

CHAPTER 10THE FUNDAMENTALS OF EXPERIMENTAL DESIGN477

10-1 What is Experimental Design?478

10-2 Examples of Designed Experiments in Quality and Process Improvement479

10-3 Experiments with One Factor483

10-3.1An Example483

10-3.2 The Analysis of Variance485

10-3.3 Residual Analysis490

10-3.4 Comparison of Individual Means491

10-3.5 Using the Computer494

10-3.6 A Components-of-Variance Model496

10-4 Blocking and Nuisance Factors499

10-4.1The Randomized Block Design499

10-4.2 Residual Analysis504

10-5 Guidelines for Designing Experiments506

10-6 Exercises508

CHAPTER 11FACTORIAL AND FRACTIONAL FACTORIAL EXPERIMENTS FOR PROCESS DESIGN AND IMPROVEMENT512

11-1 Factorial Experiments513

11-1.1An Example517

11-1.2 Statistical Analysis517

11-1.3 Residual Analysis523

11-2 The 2k Factorial Design525

11-2.1The 2 2 Design525

11-2.2 The 2k Design for k ≥ 3 Factors532

11-2.3 A Single Replicate of the 2k Design545

11-2.4 Addition of Center Points to the 2k Design549

11-2.5 Blocking and Confounding in the 2k Design553

11-3 Fractional Replication of the 2k Design555

11-3.1The One-Half Fraction of the 2k555

11-3.2 Smaller Fractions: The 2k-p Fractional Factorial Design562

11-4 Exercises569

CHAPTER 12PROCESS OPTIMIZATION WITH DESIGNED EXPERIMENTS572

12-1 Response Surface Methods and Designs573

12-1.1The Method of Steepest Ascent575

12-1.2 Analysis of a Second-Order Response Surface578

12-2 Evolutionary Operation583

12-3 Taguchi's Contributions to Quality Engineering589

12-3.1The Taguchi Philosophy590

12-3.2 The Taguchi Approach to Parameter Design591

12-3.3 Improved Robust Parameter Design600

12-4 Exercises601

PART ⅣACCEPTANCE SAMPLING605

CHAPTER 13LOT-BY-LOT ACCEPTANCE SAMPLING FOR ATTRIBUTES606

13-1 The Acceptance Sampling Problem607

13-1.1Advantages and Disadvantages of Sampling608

13-1.2 Types of Sampling Plans609

13-1.3 Lot Formation610

13-1.4 Random Sampling610

13-1.5 Guidelines for Using Acceptance Sampling611

13-2 Single-Sampling Plans for Attributes613

13-2.1Definition of a Single-Sampling Plan613

13-2.2 The OC Curve613

13-2.3 Designing a Single-Sampling Plan with a Specified OC Curve619

13-2.4 Rectifying Inspection621

13-3 Double, Multiple, and Sequential Sampling625

13-3.1Double-Sampling Plans625

13-3.2 Multiple-Sampling Plans632

13-3.3 Sequential-Sampling Plans632

13-4 Military Standard 105E （ANSI/ASQC Z1.4, ISO 2859）636

13-4.1Description of the Standard636

13-4.2 Procedure638

13-4.3 Discussion643

13-5 The Dodge-Romig Sampling Plans645

13-5.1AOQL Plans646

13-5.2 LTPD Plans648

13-5.3 Estimation of Process Average649

13-6 Exercises649

CHAPTER 14OTHER ACCEPTANCE SAMPLING TECHNIQUES652

14-1 Acceptance Sampling by Variables653

14-1.1Advantages and Disadvantages of Variables Sampling653

14-1.2 Types of Sampling Plans Available654

14-1.3 Caution in the Use of Variables Sampling655

14-2 Designing a Variables Sampling Plan with a Specified OCCurve656

14-3 MIL STD 414 （ANSI/ASQC Z1.9）659

14-3.1General Description of the Standard659

14-3.2 Use of the Tables660

14-3.3 Discussion of MIL STD 414 and ANSI/ASQC Z1.9663

14-4 Other Variables Sampling Procedures664

14-4.1Sampling by Variables to Give Assurance Regarding the Lot or Process Mean664

14-4.2 Sequential Sampling by Variables665

14-5 Chain Sampling665

14-6 Continuous Sampling667

14-6.1CSP-1668

14-6.2 Other Continuous Sampling Plans670

14-7 Skip-Lot Sampling Plans671

14-8 Exercises675

APPENDIXA-1679

Ⅰ Cumulative Poisson Distribution A-3681

Ⅱ Cumulative Standard Normal Distribution A-6684

Ⅲ Percentage Points of the x2 Distribution A-8686

Ⅳ Percentage Points of the t Distribution A-9687

Ⅴ Percentage Points of the F Distribution A-10688

Ⅵ Factors for Constructing Variables Control Charts A-15693

Ⅶ Factors for Two-Sided Normal Tolerance Limits A-16694

Ⅷ Factors for One-Sided Normal Tolerance Limits A-17695

Ⅸ Random Numbers A-18696

BIBLIOGRAPHYB-1697

ANSWERS TO SELECTED EXERCISESANS-1707

INDEXI-1719