Introduction to statistical quality control Third EditionPDF电子书下载

CHAPTER 1 QUALITY IMPROVEMENT IN THE MODERN BUSINESS ENVIRONMENT 1

1-1 The Meaning of Quality and Quality Improvement 2

1-1.1 Dimensions of Quality 2

1-1.2 Quality Engineering Terminology 6

1-2 A Brief History of Quality Methodology 8

1-3 Statistical Methods for Quality Improvement 12

1-4 Total Quality Management 17

1-4.1 Quality Philosophy 17

1-4.2 The Link Between Quality and Productivity 21

1-4.3 Quality Costs 22

1-4.4 Legal Aspects of Quality 28

1-4.5 Implementing Quality Improvement 30

PART Ⅰ STATISTICAL METHODS USEFUL IN QUALITY IMPROVEMENT 33

CHAPTER 2 MODELING PROCESS QUALITY 34

2-1 Describing Variation 35

2-1.1 The Stem and Leaf Plot 35

2-1.2 The Frequency Distribution and Histogram 38

2-1.3 Numerical Summary of Data 40

2-1.4 The Box Plot 43

2-1.5 Sample Computer Output 44

2-1.6 Probability Distributions 46

2-2 Important Discrete Distributions 51

2-2.1 The Hypergeometric Distribution 51

2-2.2 The Binomial Distribution 52

2-2.3 The Poisson Distribution 55

2-2.4 The Pascal and Related Distributions 56

2-3 Important Continuous Distributions 57

2-3.1 The Normal Distribution 57

2-3.2 The Exponential Distribution 62

2-3.3 The Gamma Distribution 65

2-3.4 The Weibull Distribution 67

2-4 Some Useful Approximations 69

2-4.1 The Binomial Approximation to the Hypergeometric 69

2-4.2 The Poisson Approximation to the Binomial 69

2-4.3 The Normal Approximation to the Binomial 70

2-4.4 Comments on Approximations 70

2-5 Exercises 71

CHAPTER 3 INFERENCES ABOUT PROCESS QUALITY 77

3-1 Statistics and Sampling Distributions 78

3-1.1 Sampling from a Normal Distribution 79

3-1.2 Sampling from a Bernoulli Distribution 83

3-1.3 Sampling from a Poisson Distribution 84

3-2 Estimation of Process Parameters 85

3-2.1 Point Estimation 85

3-2.2 Interval Estimation 86

3-3 Hypothesis Testing on Process Parameters 96

3-3.1 Tests on Means, Variance Known 97

3-3.2 The Use of P-Values in Hypothesis Testing 100

3-3.3 Tests on Means of Normal Distributions, Variance Unknown 101

3-3.4 Tests on Variances of Normal Distributions 107

3-3.5 Tests on Binomial Parameters 109

3-3.6 Tests on Poisson Parameters 110

3-3.7 Probability Plotting 113

3-3.8 The Probability of Type Ⅱ Error 116

3-4 Exercises 119

PART Ⅱ STATISTICAL PROCESS CONTROL 127

CHAPTER 4 METHODS AND PHILOSOPHY OF STATISTICAL PROCESS CONTROL 129

4-1 Introduction 130

4-2 Chance and Assignable Causes of Quality Variation 130

4-3 Statistical Basis of the Control Chart 132

4-3.1 Basic Principles 132

4-3.2 Choice of Control Limits 138

4-3.3 Sample Size and Sampling Frequency 140

4-3.4 Rational Subgroups 143

4-3.5 Analysis of Patterns on Control Charts 146

4-3.6 Discussion of Sensitizing Rules for Control Charts 149

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

4-5 Implementing SPC 158

4-6 An Application of SPC 159

4-7 Nonmanufacturing Applications of Statistical Process Control 167

4-8 Exercises 174

CHAPTER 5 CONTROL CHARTS FOR VARIABLES 179

5-1 Introduction 180

5-2 Control Charts for x and R 181

5-2.1 Statistical Basis of the Charts 181

5-2.2 Development and Use of x and R Charts 186

5-2.3 Charts Based onStandard Values 201

5-2.4 Interpretation of x and R Charts 202

5-2.5 The Effect of Nonnormality on x and R Charts 205

5-2.6 The Operating-Characteristic Function 206

5-2.7 The Average Run Length for the x Chart 209

5-3 Control Charts for x and S 211

5-3.1 Construction and Operation of x and S Charts 212

5-3.2 The x and S Control Charts with Variable Sample Size 217

5-3.3 The S2 Control Chart 221

5-4 Control Charts for Individual Measurements 221

5-5 Summary of Procedures for x, R, and S Charts 229

5-6 Applications of Variables Control Charts 230

5-7 Exercises 235

CHAPTER 6 CONTROL CHARTS FOR ATTRIBUTES 250

6-1 Introduction 251

6-2 The Control Chart for Fraction Nonconforming 251

6-2.1 Development and Operation of the Control Chart 253

6-2.2 Variable Sample Size 265

6-2.3 Nonmanufacturing Applications 270

6-2.4 The Operating-Characteristic Function and Average Run Length Calculations 271

6-3 Control Charts for Nonconformities （Defects） 275

6-3.1 Procedures with Constant Sample Size 275

6-3.2 Procedures with Variable Sample Size 285

6-3.3 Demerit Systems 287

6-3.4 The Operating-Characteristic Function 289

6-3.5 Dealing with Low Defect Levels 290

6-3.6 Nonmanufacturing Applications 294

6-4 Choice Between Attributes and Variables Control Charts 294

6-5 Guidelines for Implementing Control Charts 299

6-6 Exercises 304

CHAPTER 7 CUMULATIVE SUM AND EXPONENTIALLY WEIGHTED MOVING AVERAGE CONTROL CHARTS 313

7-1 The Cumulative-Sum Control Chart 314

7-1.1 Basic Principles: The Cusum Control Chart for Monitoring the Process Mean 314

7-1.2 The Tabular or Algorithmic Cusum for Monitoring the Process Mean 317

7-1.3 Recommendations for Cusum Design 322

7-1.4 The Standardized Cusum 324

7-1.5 Rational Subgroups 325

7-1.6 Improving Cusum Responsiveness for Large Shifts 325

7-1.7 The Fast Initial Response or Headstart Feature 325

7-1.8 One-Sided Cusums 327

7-1.9 A Cusum for Monitoring Process Variability 328

7-1.10 Cusums for Other Sample Statistics 329

7-1.11 The V-Mask Procedure 329

7-2 The Exponentially Weighted Moving-Average Control Chart 332

7-2.1 The Exponentially Weighted Moving-Average Control Chart for Monitoring the Process Mean 333

7-2.2 Design of an EWMA Control Chart 337

7-2.3 Rational Subgroups 339

7-2.4 Extensions of the EWMA 339

7-3 The Moving Average Control Chart 341

7-4 Exercises 344

CHAPTER 8 OTHER STATISTICAL PROCESS CONTROL TECHNIQUES 348

8-1 Statistical Process Control for Short Production Runs 349

8-1.1 x and R Charts for Short Production Runs 349

8-1.2 Attribute Control Charts for Short Production Runs 352

8-1.3 Other Methods 353

8-2 Modified and Acceptance Control Charts 354

8-2.1 Modified Control Limits for the x Chart 354

8-2.2 Acceptance Control Charts 357

8-3 Group Control Charts for Multiple-Stream Processes 358

8-4 Multivariate Quality Control 360

8-4.1 Monitoring of Means 362

8-4.2 Monitoring Process Variability 372

8-5 SPC with Correlated Data 374

8-6 Interfacing Statistical Process Control and Engineering Process Control 386

8-6.1 Process Monitoring and Process Regulation 386

8-6.2 Combining SPC and EPC 395

8-7 Economic Design of Control Charts 399

8-7.1 Designing a Control Chart 399

8-7.2 Process Characteristics 399

8-7.3 Cost Parameters 400

8-7.4 Early Work and Semi-Economic Design 402

8-7.5 An Economic Model of the x Control Chart 403

8-7.6 Other Work 412

8-8 Overview of Other Procedures 413

8-8.1 Tool Wear 413

8-8.2 Control Charts Based on Other Sample Statistics 414

8-8.3 Adaptive Schemes 415

8-8.4 Selecting the Optimum Target Value for a Process 417

8-8.5 Fill Control 419

8-8.6 Precontrol 419

8-9 Exercises 421

CHAPTER 9 PROCESS CAPABILITY ANALYSIS 430

9-1 Introduction 431

9-2 Process-Capability Analysis Using a Histogram or a Probability Plot 433

9-2.1 Using the Histogram 433

9-2.2 Probability Plotting 434

9-3 Process Capability Ratios 438

9-3.1 Use and Interpretation of PCR 438

9-3.2 Process-Capability Ratio for an Off-Center Process 442

9-3.3 Normality and the Process Capability Ratio 444

9-3.4 More About Process Centering 444

9-3.5 Confidence Intervals and Tests on Process Capability Ratios 447

9-4 Process-Capability Analysis Using a Control Chart 451

9-5 Process-Capability Analysis Using Designed Experiments 453

9-6 Gage and Measurement System Capability Studies 455

9-7 Setting Specification Limits on Discrete Components 461

9-7.1 Linear Combinations 461

9-7.2 Nonlinear Combinations 465

9-8 Estimating the Natural Tolerance Limits of a Process 467

9-8.1 Tolerance Limits Based on the Normal Distribution 468

9-8.2 Nonparametric Tolerance Limits 469

9-9 Exercises 470

PART Ⅲ PROCESS DESIGN AND IMPROVEMENT WITH DESIGNED EXPERIMENTS 475

CHAPTER 10 THE FUNDAMENTALS OF EXPERIMENTAL DESIGN 477

10-1 What is Experimental Design? 478

10-2 Examples of Designed Experiments in Quality and Process Improvement 479

10-3 Experiments with One Factor 483

10-3.1 An Example 483

10-3.2 The Analysis of Variance 485

10-3.3 Residual Analysis 490

10-3.4 Comparison of Individual Means 491

10-3.5 Using the Computer 494

10-3.6 A Components-of-Variance Model 496

10-4 Blocking and Nuisance Factors 499

10-4.1 The Randomized Block Design 499

10-4.2 Residual Analysis 504

10-5 Guidelines for Designing Experiments 506

10-6 Exercises 508

CHAPTER 11 FACTORIAL AND FRACTIONAL FACTORIAL EXPERIMENTS FOR PROCESS DESIGN AND IMPROVEMENT 512

11-1 Factorial Experiments 513

11-1.1 An Example 517

11-1.2 Statistical Analysis 517

11-1.3 Residual Analysis 523

11-2 The 2k Factorial Design 525

11-2.1 The 2 2 Design 525

11-2.2 The 2k Design for k ≥ 3 Factors 532

11-2.3 A Single Replicate of the 2k Design 545

11-2.4 Addition of Center Points to the 2k Design 549

11-2.5 Blocking and Confounding in the 2k Design 553

11-3 Fractional Replication of the 2k Design 555

11-3.1 The One-Half Fraction of the 2k 555

11-3.2 Smaller Fractions: The 2k-p Fractional Factorial Design 562

11-4 Exercises 569

CHAPTER 12 PROCESS OPTIMIZATION WITH DESIGNED EXPERIMENTS 572

12-1 Response Surface Methods and Designs 573

12-1.1 The Method of Steepest Ascent 575

12-1.2 Analysis of a Second-Order Response Surface 578

12-2 Evolutionary Operation 583

12-3 Taguchi's Contributions to Quality Engineering 589

12-3.1 The Taguchi Philosophy 590

12-3.2 The Taguchi Approach to Parameter Design 591

12-3.3 Improved Robust Parameter Design 600

12-4 Exercises 601

PART Ⅳ ACCEPTANCE SAMPLING 605

CHAPTER 13 LOT-BY-LOT ACCEPTANCE SAMPLING FOR ATTRIBUTES 606

13-1 The Acceptance Sampling Problem 607

13-1.1 Advantages and Disadvantages of Sampling 608

13-1.2 Types of Sampling Plans 609

13-1.3 Lot Formation 610

13-1.4 Random Sampling 610

13-1.5 Guidelines for Using Acceptance Sampling 611

13-2 Single-Sampling Plans for Attributes 613

13-2.1 Definition of a Single-Sampling Plan 613

13-2.2 The OC Curve 613

13-2.3 Designing a Single-Sampling Plan with a Specified OC Curve 619

13-2.4 Rectifying Inspection 621

13-3 Double, Multiple, and Sequential Sampling 625

13-3.1 Double-Sampling Plans 625

13-3.2 Multiple-Sampling Plans 632

13-3.3 Sequential-Sampling Plans 632

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

13-4.1 Description of the Standard 636

13-4.2 Procedure 638

13-4.3 Discussion 643

13-5 The Dodge-Romig Sampling Plans 645

13-5.1 AOQL Plans 646

13-5.2 LTPD Plans 648

13-5.3 Estimation of Process Average 649

13-6 Exercises 649

CHAPTER 14 OTHER ACCEPTANCE SAMPLING TECHNIQUES 652

14-1 Acceptance Sampling by Variables 653

14-1.1 Advantages and Disadvantages of Variables Sampling 653

14-1.2 Types of Sampling Plans Available 654

14-1.3 Caution in the Use of Variables Sampling 655

14-2 Designing a Variables Sampling Plan with a Specified OCCurve 656

14-3 MIL STD 414 （ANSI/ASQC Z1.9） 659

14-3.1 General Description of the Standard 659

14-3.2 Use of the Tables 660

14-3.3 Discussion of MIL STD 414 and ANSI/ASQC Z1.9 663

14-4 Other Variables Sampling Procedures 664

14-4.1 Sampling by Variables to Give Assurance Regarding the Lot or Process Mean 664

14-4.2 Sequential Sampling by Variables 665

14-5 Chain Sampling 665

14-6 Continuous Sampling 667

14-6.1 CSP-1 668

14-6.2 Other Continuous Sampling Plans 670

14-7 Skip-Lot Sampling Plans 671

14-8 Exercises 675

APPENDIX A-1 679

Ⅰ Cumulative Poisson Distribution A-3 681

Ⅱ Cumulative Standard Normal Distribution A-6 684

Ⅲ Percentage Points of the x2 Distribution A-8 686

Ⅳ Percentage Points of the t Distribution A-9 687

Ⅴ Percentage Points of the F Distribution A-10 688

Ⅵ Factors for Constructing Variables Control Charts A-15 693

Ⅶ Factors for Two-Sided Normal Tolerance Limits A-16 694

Ⅷ Factors for One-Sided Normal Tolerance Limits A-17 695

Ⅸ Random Numbers A-18 696

BIBLIOGRAPHY B-1 697

ANSWERS TO SELECTED EXERCISES ANS-1 707

INDEX I-1 719