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![كتاب Introduction to Statistical Quality Control - Sixth Edition Empty](https://2img.net/i/fa/empty.gif) | موضوع: كتاب Introduction to Statistical Quality Control - Sixth Edition السبت 08 فبراير 2014, 1:29 am | |
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أخوانى فى الله أحضرت لكم كتاب Introduction to Statistical Quality Control - Sixth Edition DOUGLAS C. MONTGOMERY Arizona State University
![كتاب Introduction to Statistical Quality Control - Sixth Edition I_s_q_10](https://i.servimg.com/u/f61/15/36/74/14/i_s_q_10.jpg) ويتناول الموضوعات الأتية :
Contents PART 1 INTRODUCTION QUALITY IMPROVEMENT IN THE MODERN BUSINESS ENVIRONMENT 3 Chapter Overview and Learning Objectives 3 1.1 The Meaning of Quality and Quality Improvement 4 1.1.1 Dimensions of Quality 4 1.1.2 Quality Engineering Terminology 8 1.2 A Brief History of Quality Control and Improvement 9 1.3 Statistical Methods for Quality Control and Improvement 13 1.4 Management Aspects of Quality Improvement 16 1.4.1 Quality Philosophy and Management Strategies 17 1.4.2 The Link Between Quality and Productivity 35 1.4.3 Quality Costs 36 1.4.4 Legal Aspects of Quality 41 1.4.5 Implementing Quality Improvement 42 2 THE DMAIC PROCESS 45 Chapter Overview and Learning Objectives 45 2.1 Overview of DMAIC 45 2.2 The Define Step 49 2.3 The Measure Step 50 2.4 The Analyze Step 52 2.5 The Improve Step 53 2.6 The Control Step 54 2.7 Examples of DMAIC 54 2.7.1 Litigation Documents 54 2.7.2 Improving On-Time Delivery 56 2.7.3 Improving Service Quality in a Bank 59 PART 2 STATISTICAL METHODS USEFUL IN QUALITY CONTROL AND IMPROVEMENT 61 3 MODELING PROCESS QUALITY 63 Chapter Overview and Learning Objectives 63 3.1 Describing Variation 64 3.1.1 The Stem-and-Leaf Plot 64 3.1.2 The Histogram 66 3.1.3 Numerical Summary of Data 69 3.1.4 The Box Plot 71 3.1.5 Probability Distributions 72 3.2 Important Discrete Distributions 76 3.2.1 The Hypergeometric Distribution 76 3.2.2 The Binomial Distribution 77 3.2.3 The Poisson Distribution 79 3.2.4 The Pascal and Related Distributions 80 3.3 Important Continuous Distributions 81 3.3.1 The Normal Distribution 81 3.3.2 The Lognormal Distribution 86 3.3.3 The Exponential Distribution 88 3.3.4 The Gamma Distribution 89 3.3.5 The Weibull Distribution 91 3.4 Probability Plots 93 3.4.1 Normal Probability Plots 93 3.4.2 Other Probability Plots 95 3.5 Some Useful Approximations 96 3.5.1 The Binomial Approximation to the Hypergeometric 963.5.2 The Poisson Approximation to the Binomial 96 3.5.3 The Normal Approximation to the Binomial 97 3.5.4 Comments on Approximations 98 4 INFERENCES ABOUT PROCESS QUALITY 103 Chapter Overview and Learning Objectives 104 4.1 Statistics and Sampling Distributions 104 4.1.1 Sampling from a Normal Distribution 105 4.1.2 Sampling from a Bernoulli Distribution 108 4.1.3 Sampling from a Poisson Distribution 109 4.2 Point Estimation of Process Parameters 110 4.3 Statistical Inference for a Single Sample 112 4.3.1 Inference on the Mean of a Population, Variance Known 113 4.3.2 The Use of P-Values for Hypothesis Testing 116 4.3.3 Inference on the Mean of a Normal Distribution, Variance Unknown 117 4.3.4 Inference on the Variance of a Normal Distribution 120 4.3.5 Inference on a Population Proportion 122 4.3.6 The Probability of Type II Error and Sample Size Decisions 124 4.4 Statistical Inference for Two Samples 127 4.4.1 Inference for a Difference in Means, Variances Known 128 4.4.2 Inference for a Difference in Means of Two Normal Distributions, Variances Unknown 130 4.4.3 Inference on the Variances of Two Normal Distributions 137 4.4.4 Inference on Two Population Proportions 139 4.5 What If There Are More Than Two Populations? The Analysis of Variance 140 4.5.1 An Example 140 4.5.2 The Analysis of Variance 142 4.5.3 Checking Assumptions: Residual Analysis 148 4.6 Linear Regression Models 150 4.6.1 Estimation of the Parameters in Linear Regression Models 151 x Contents 4.6.2 Hypothesis Testing in Multiple Regression 157 4.6.3 Confidance Intervals in Multiple Regression 163 4.6.4 Prediction of New Observations 164 4.6.5 Regression Model Diagnostics 165 PART 3 BASIC METHODS OF STATISTICAL PROCESS CONTROL AND CAPABILITY ANALYSIS 177 5 METHODS AND PHILOSOPHY OF STATISTICAL PROCESS CONTROL 179 Chapter Overview and Learning Objectives 179 5.1 Introduction 180 5.2 Chance and Assignable Causes of Quality Variation 181 5.3 Statistical Basis of the Control Chart 182 5.3.1 Basic Principles 182 5.3.2 Choice of Control Limits 189 5.3.3 Sample Size and Sampling Frequency 191 5.3.4 Rational Subgroups 193 5.3.5 Analysis of Patterns on Control Charts 195 5.3.6 Discussion of Sensitizing Rules for Control Charts 197 5.3.7 Phase I and Phase II of Control Chart Application 198 5.4 The Rest of the Magnificent Seven 199 5.5 Implementing SPC in a Quality Improvement Program 205 5.6 An Application of SPC 206 5.7 Applications of Statistical Process Control and Quality Improvement Tools in Transactional and Service Businesses 213 6 CONTROL CHARTS FOR VARIABLES 226 Chapter Overview and Learning Objectives 226 6.1 Introduction 2276.2 Control Charts for – x and R 228 6.2.1 Statistical Basis of the Charts 228 6.2.2 Development and Use of – x and R Charts 231 6.2.3 Charts Based on Standard Values 242 6.2.4 Interpretation of – x and R Charts 243 6.2.5 The Effect of Nonnormality on – x and R Charts 246 6.2.6 The Operating-Characteristic Function 246 6.2.7 The Average Run Length for the – x Chart 249 6.3 Control Charts for – x and s 251 6.3.1 Construction and Operation of – x and s Charts 251 6.3.2 The – x and s Control Charts with Variable Sample Size 255 6.3.3 The s2 Control Chart 259 6.4 The Shewhart Control Chart for Individual Measurements 259 6.5 Summary of Procedures for – x, R, and s Charts 268 6.6 Applications of Variables Control Charts 268 7 CONTROL CHARTS FOR ATTRIBUTES 288 Chapter Overview and Learning Objectives 288 7.1 Introduction 289 7.2 The Control Chart for Fraction Nonconforming 289 7.2.1 Development and Operation of the Control Chart 290 7.2.2 Variable Sample Size 301 7.2.3 Applications in Transactional and Service Businesses 304 7.2.4 The Operating-Characteristic Function and Average Run Length Calculations 306 7.3 Control Charts for Nonconformities (Defects) 308 7.3.1 Procedures with Constant Sample Size 309 7.3.2 Procedures with Variable Sample Size 319 7.3.3 Demerit Systems 321 Contents xi 7.3.4 The Operating-Characteristic Function 322 7.3.5 Dealing with Low Defect Levels 323 7.3.6 Nonmanufacturing Applications 326 7.4 Choice Between Attributes and Variables Control Charts 326 7.5 Guidelines for Implementing Control Charts 330 8 PROCESS AND MEASUREMENT SYSTEM CAPABILITY ANALYSIS 344 Chapter Overview and Learning Objectives 345 8.1 Introduction 345 8.2 Process Capability Analysis Using a Histogram or a Probability Plot 347 8.2.1 Using the Histogram 347 8.2.2 Probability Plotting 349 8.3 Process Capability Ratios 351 8.3.1 Use and Interpretation of C p 351 8.3.2 Process Capability Ratio for an Off-Center Process 354 8.3.3 Normality and the Process Capability Ratio 356 8.3.4 More about Process Centering 357 8.3.5 Confidence Intervals and Tests on Process Capability Ratios 359 8.4 Process Capability Analysis Using a Control Chart 364 8.5 Process Capability Analysis Using Designed Experiments 366 8.6 Process Capability Analysis with Attribute Data 367 8.7 Gauge and Measurement System Capability Studies 368 8.7.1 Basic Concepts of Gauge Capability 368 8.7.2 The Analysis of Variance Method 373 8.7.3 Confidence Intervals in Gauge R & R Studies 376 8.7.4 False Defectives and Passed Defectives 377 8.7.5 Attribute Gauge Capability 381 8.8 Setting Specification Limits on Discrete Components 383 8.8.1 Linear Combinations 384 8.8.2 Nonlinear Combinations 3878.9 Estimating the Natural Tolerance Limits of a Process 388 8.9.1 Tolerance Limits Based on the Normal Distribution 389 8.9.2 Nonparametric Tolerance Limits 390 PART 4 OTHER STATISTICAL PROCESSMONITORING AND CONTROL TECHNIQUES 397 9 CUMULATIVE SUM AND EXPONENTIALLY WEIGHTED MOVING AVERAGE CONTROL CHARTS 399 Chapter Overview and Learning Objectives 400 9.1 The Cumulative Sum Control Chart 400 9.1.1 Basic Principles: The Cusum Control Chart for Monitoring the Process Mean 400 9.1.2 The Tabular or Algorithmic Cusum for Monitoring the Process Mean 403 9.1.3 Recommendations for Cusum Design 408 9.1.4 The Standardized Cusum 410 9.1.5 Improving Cusum Responsiveness for Large Shifts 410 9.1.6 The Fast Initial Response or Headstart Feature 410 9.1.7 One-Sided Cusums 413 9.1.8 A Cusums for Monitoring Process Variability 413 9.1.9 Rational Subgroups 414 9.1.10 Cusums for Other Sample Statistics 414 9.1.11 The V-Mask Procedure 415 9.1.12 The Self-Starting Cusum 417 9.2 The Exponentially Weighted Moving Average Control Chart 419 9.2.1 The Exponentially Weighted Moving Average Control Chart for Monitoring the Process Mean 419 xii Contents 9.2.2 Design of an EWMA Control Chart 422 9.2.3 Robustness of the EWMA to Nonnormality 424 9.2.4 Rational Subgroups 425 9.2.5 Extensions of the EWMA 425 9.3 The Moving Average Control Chart 428 10 OTHER UNIVARIATE STATISTICAL PROCESS MONITORING AND CONTROL TECHNIQUES 433 Chapter Overview and Learning Objectives 434 10.1 Statistical Process Control for Short Production Runs 435 10.1.1 – x and R Charts for Short Production Runs 435 10.1.2 Attributes Control Charts for Short Production Runs 437 10.1.3 Other Methods 437 10.2 Modified and Acceptance Control Charts 439 10.2.1 Modified Control Limits for the – x Chart 439 10.2.2 Acceptance Control Charts 442 10.3 Control Charts for Multiple-Stream Processes 443 10.3.1 Multiple-Stream Processes 443 10.3.2 Group Control Charts 443 10.3.3 Other Approaches 445 10.4 SPC With Autocorrelated Process Data 446 10.4.1 Sources and Effects of Autocorrelation in Process Data 446 10.4.2 Model-Based Approaches 450 10.4.3 A Model-Free Approach 458 10.5 Adaptive Sampling Procedures 462 10.6 Economic Design of Control Charts 463 10.6.1 Designing a Control Chart 463 10.6.2 Process Characteristics 464 10.6.3 Cost Parameters 464 10.6.4 Early Work and Semieconomic Designs 466 10.6.5 An Economic Model of the – x Control Chart 467 10.6.6 Other Work 472 10.7 Cuscore Charts 473 10.8 The Changepoint Model for Process Monitoring 475 10.9 Profile Monitoring 47610.10 Control Charts in Health Care Monitoring and Public Health Surveillance 481 10.11 Overview of Other Procedures 482 10.11.1 Tool Wear 482 10.11.2 Control Charts Based on Other Sample Statistics 482 10.11.3 Fill Control Problems 484 10.11.4 Precontrol 484 10.11.5 Tolerance Interval Control Charts 485 10.11.6 Monitoring Processes with Censored Data 486 10.11.7 Nonparametric Control Charts 487 11 MULTIVARIATE PROCESS MONITORING AND CONTROL 494 Chapter Overview and Learning Objectives 494 11.1 The Multivariate Quality-Control Problem 495 11.2 Description of Multivariate Data 497 11.2.1 The Multivariate Normal Distribution 497 11.2.2 The Sample Mean Vector and Covariance Matrix 498 11.3 The Hotelling T 2 Control Chart 499 11.3.1 Subgrouped Data 499 11.3.2 Individual Observations 506 11.4 The Multivariate EWMA Control Chart 509 11.5 Regression Adjustment 513 11.6 Control Charts for Monitoring Variability 516 11.7 Latent Structure Methods 518 11.7.1 Principal Components 518 11.7.2 Partial Least Squares 523 12 ENGINEERING PROCESS CONTROL AND SPC 527 Chapter Overview and Learning Objectives 527 12.1 Process Monitoring and Process Regulation 528 12.2 Process Control by Feedback Adjustment 529 12.2.1 A Simple Adjustment Scheme: Integral Control 529 12.2.2 The Adjustment Chart 534 12.2.3 Variations of the Adjustment Chart 536 Contents xiii 12.2.4 Other Types of Feedback Controllers 539 12.3 Combining SPC and EPC 540 PART 5 PROCESS DESIGN AND IMPROVEMENT WITH DESIGNED EXPERIMENTS 547 13 FACTORIAL AND FRACTIONAL FACTORIAL EXPERIMENTS FOR PROCESS DESIGN AND IMPROVEMENT 549 Chapter Overview and Learning Objectives 550 13.1 What is Experimental Design? 550 13.2 Examples of Designed Experiments In Process and Product Improvement 552 13.3 Guidelines for Designing Experiments 554 13.4 Factorial Experiments 556 13.4.1 An Example 558 13.4.2 Statistical Analysis 558 13.4.3 Residual Analysis 563 13.5 The 2k Factorial Design 564 13.5.1 The 22 Design 564 13.5.2 The 2k Design for k ? 3 Factors 569 13.5.3 A Single Replicate of the 2k Design 579 13.5.4 Addition of Center Points to the 2k Design 582 13.5.5 Blocking and Confounding in the 2k Design 585 13.6 Fractional Replication of the 2k Design 587 13.6.1 The One-Half Fraction of the 2k Design 587 13.6.2 Smaller Fractions: The 2k–p Fractional Factorial Design 592 14 PROCESS OPTIMIZATION WITH DESIGNED EXPERIMENTS 602 Chapter Overview and Learning Objectives 602 14.1 Response Surface Methods and Designs 603 14.1.1 The Method of Steepest Ascent 60514.1.2 Analysis of a Second-Order Response Surface 607 14.2 Process Robustness Studies 611 14.2.1 Background 611 14.2.2 The Response Surface Approach to Process Robustness Studies 613 14.3 Evolutionary Operation 619 PART 6 ACCEPTANCE SAMPLING 629 15 LOT-BY-LOT ACCEPTANCE SAMPLING FOR ATTRIBUTES 631 Chapter Overview and Learning Objectives 631 15.1 The Acceptance-Sampling Problem 632 15.1.1 Advantages and Disadvantages of Sampling 633 15.1.2 Types of Sampling Plans 634 15.1.3 Lot Formation 635 15.1.4 Random Sampling 635 15.1.5 Guidelines for Using Acceptance Sampling 636 15.2 Single-Sampling Plans for Attributes 637 15.2.1 Definition of a Single-Sampling Plan 637 15.2.2 The OC Curve 637 15.2.3 Designing a Single-Sampling Plan with a Specified OC Curve 642 15.2.4 Rectifying Inspection 643 15.3 Double, Multiple, and Sequential Sampling 646 15.3.1 Double-Sampling Plans 647 15.3.2 Multiple-Sampling Plans 651 15.3.3 Sequential-Sampling Plans 652 15.4 Military Standard 105E (ANSI/ ASQC Z1.4, ISO 2859) 655 15.4.1 Description of the Standard 655 15.4.2 Procedure 657 15.4.3 Discussion 661 15.5 The Dodge–Romig Sampling Plans 663 15.5.1 AOQL Plans 664 15.5.2 LTPD Plans 667 15.5.3 Estimation of Process Average 667 xiv Contents 16 OTHER ACCEPTANCE-SAMPLING TECHNIQUES 670 Chapter Overview and Learning Objectives 670 16.1 Acceptance Sampling by Variables 671 16.1.1 Advantages and Disadvantages of Variables Sampling 671 16.1.2 Types of Sampling Plans Available 672 16.1.3 Caution in the Use of Variables Sampling 673 16.2 Designing a Variables Sampling Plan with a Specified OC Curve 673 16.3 MIL STD 414 (ANSI/ASQC Z1.9) 676 16.3.1 General Description of the Standard 676 16.3.2 Use of the Tables 677 16.3.3 Discussion of MIL STD 414 and ANSI/ASQC Z1.9 679 16.4 Other Variables Sampling Procedures 680 16.4.1 Sampling by Variables to Give Assurance Regarding the Lot or Process Mean 680 16.4.2 Sequential Sampling by Variables 681 16.5 Chain Sampling 681 16.6 Continuous Sampling 683 16.6.1 CSP-1 683 16.6.2 Other Continuous-Sampling Plans 686 16.7 Skip-Lot Sampling Plans 686 APPENDIX 691 I. Summary of Common Probability Distributions Often Used in Statistical Quality Control 692 II. Cumulative Standard Normal Distribution 693 III. Percentage Points of the ?2 Distribution 695 IV. Percentage Points of the t Distribution 696 V. Percentage Points of the F Distribution 697 VI. Factors for Constructing Variables Control Charts 702 VII. Factors for Two-Sided Normal Tolerance Limits 703 VIII. Factors for One-Sided Normal Tolerance Limits 704 BIBLIOGRAPHY 7
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