Introduction to statistical quality control

• 作者： Montgomery, Douglas C.
• 出版： Hoboken, NJ : Wiley
• 版本：7th ed.
• 主題： Quality control--Statistical methods , Process control--Statistical methods
• ISBN： 9781118146811 (hbk.): US$207.88 、 1118146816 、 9781118322574 、 1118322576
• 資料類型: 圖書
• 課程: 品質工程 ( 奈米工程設計碩士學程 - 陳始明)
• 內容註: Includes bibliographical references (p. 723-737) and index. PART 1 Introduction -- 1 Quality Improvement in the Modern Business Environment -- Chapter Overview and Learning Objectives -- 1.1 The Meaning of Quality and Quality Improvement -- 1.1.1 Dimensions of Quality -- 1.1.2 Quality Engineering Terminology -- 1.2 A Brief History of Quality Control and Improvement -- 1.3 Statistical Methods for Quality Control and Improvement -- 1.4 Management Aspects of Quality Improvement -- 1.4.1 Quality Philosophy and Management Strategies -- 1.4.2 The Link Between Quality and Productivity -- 1.4.3 Supply Chain Quality Management -- 1.4.4 Quality Costs -- 1.4.5 Legal Aspects of Quality -- 1.4.6 Implementing Quality Improvement -- 2 The DMAIC Process -- Chapter Overview and Learning Objectives -- 2.1 Overview of DMAIC -- 2.2 The Define Step -- 2.3 The Measure Step -- 2.4 The Analyze Step -- 2.5 The Improve Step -- 2.6 The Control Step -- 2.7 Examples of DMAIC -- 2.7.1 Litigation Documents -- 2.7.2 Improving On-Time Delivery -- 2.7.3 Improving Service Quality in a Bank PART 2 Statistical methods useful in quality control and improvement -- 3 Modeling Process Quality -- Chapter Overview and Learning Objectives -- 3.1 Describing Variation -- 3.1.1 The Stem-and-Leaf Plot -- 3.1.2 The Histogram -- 3.1.3 Numerical Summary of Data -- 3.1.4 The Box Plot -- 3.1.5 Probability Distributions -- 3.2 Important Discrete Distributions -- 3.2.1 The Hypergeometric Distribution -- 3.2.2 The Binomial Distribution -- 3.2.3 The Poisson Distribution -- 3.2.4 The Negative Binomial and Geometric Distributions -- 3.3 Important Continuous Distributions -- 3.3.1 The Normal Distribution -- 3.3.2 The Lognormal Distribution -- 3.3.3 The Exponential Distribution -- 3.3.4 The Gamma Distribution -- 3.3.5 The Weibull Distribution -- 3.4 Probability Plots -- 3.4.1 Normal Probability Plots -- 3.4.2 Other Probability Plots -- 3.5 Some Useful Approximations -- 3.5.1 The Binomial Approximation to the Hypergeometric -- 3.5.2 The Poisson Approximation to the Binomial -- 3.5.3 The Normal Approximation to the Binomial -- 3.5.4 Comments on Approximations -- 4 Inferences about Process Quality -- Chapter Overview and Learning Objectives -- 4.1 Statistics and Sampling Distributions -- 4.1.1 Sampling from a Normal Distribution -- 4.1.2 Sampling from a Bernoulli Distribution -- 4.1.3 Sampling from a Poisson Distribution -- 4.2 Point Estimation of Process Parameters 4.3 Statistical Inference for a Single Sample -- 4.3.1 Inference on the Mean of a Population, Variance Known -- 4.3.2 The Use of P-Values for Hypothesis Testing -- 4.3.3 Inference on the Mean of a Normal Distribution, Variance Unknown -- 4.3.4 Inference on the Variance of a Normal Distribution -- 4.3.5 Inference on a Population Proportion -- 4.3.6 The Probability of Type II Error and Sample Size Decisions -- 4.4 Statistical Inference for Two Samples -- 4.4.1 Inference for a Difference in Means, Variances Known -- 4.4.2 Inference for a Difference in Means of Two Normal Distributions, Variances Unknown -- 4.4.3 Inference on the Variances of Two Normal Distributions -- 4.4.4 Inference on Two Population Proportions -- 4.5 What If There Are More Than Two Populations? The Analysis of Variance -- 4.5.1 An Example -- 4.5.2 The Analysis of Variance -- 4.5.3 Checking Assumptions: Residual Analysis -- 4.6 Linear Regression Models -- 4.6.1 Estimation of the Parameters in Linear Regression Models -- 4.6.2 Hypothesis Testing in Multiple Regression -- 4.6.3 Confidance Intervals in Multiple Regression -- 4.6.4 Prediction of New Observations -- 4.6.5 Regression Model Diagnostics PART 3 Basic methods of statistical process control and capability analysis -- 5 Methods and Philosophy of Statistical Process Control -- Chapter Overview and Learning Objectives -- 5.1 Introduction -- 5.2 Chance and Assignable Causes of Quality Variation -- 5.3 Statistical Basis of the Control Chart -- 5.3.1 Basic Principles -- 5.3.2 Choice of Control Limits -- 5.3.3 Sample Size and Sampling Frequency -- 5.3.4 Rational Subgroups -- 5.3.5 Analysis of Patterns on Control Charts -- 5.3.6 Discussion of Sensitizing Rules for Control Charts -- 5.3.7 Phase I and Phase II of Control Chart Application -- 5.4 The Rest of the Magnificent Seven -- 5.5 Implementing SPC in a Quality Improvement Program -- 5.6 An Application of SPC -- 5.7 Applications of Statistical Process Control and Quality Improvement Tools in Transactional and Service Businesses -- 6 Control Charts for Variables -- Chapter Overview and Learning Objectives -- 6.1 Introduction -- 6.2 Control Charts for -x and R -- 6.2.1 Statistical Basis of the Charts -- 6.2.2 Development and Use of -x and R Charts -- 6.2.3 Charts Based on Standard Values -- 6.2.4 Interpretation of -x and R Charts -- 6.2.5 The Effect of Nonnormality on -x and R Charts -- 6.2.6 The Operating-Characteristic Function -- 6.2.7 The Average Run Length for the -x Chart -- 6.3 Control Charts for -x and s -- 6.3.1 Const 8.3 Process Capability Ratios -- 8.3.1 Use and Interpretation of Cp -- 8.3.2 Process Capability Ratio for an Off-Center Process -- 8.3.3 Normality and the Process Capability Ratio -- 8.3.4 More about Process Centering -- 8.3.5 Confidence Intervals and Tests on Process Capability Ratios -- 8.4 Process Capability Analysis Using a Control Chart -- 8.5 Process Capability Analysis Using Designed Experiments -- 8.6 Process Capability Analysis with Attribute Data -- 8.7 Gauge and Measurement System Capability Studies -- 8.7.1 Basic Concepts of Gauge Capability -- 8.7.2 The Analysis of Variance Method -- 8.7.3 Confidence Intervals in Gauge R & R Studies -- 8.7.4 False Defectives and Passed Defectives -- 8.7.5 Attribute Gauge Capability -- 8.7.6 Comparing Customer and Supplier Measurement Systems -- 8.8 Setting Specification Limits on Discrete Components -- 8.8.1 Linear Combinations -- 8.8.2 Nonlinear Combinations -- 8.9 Estimating the Natural Tolerance Limits of a Process -- 8.9.1 Tolerance Limits Based on the Normal Distribution -- 8.9.2 Nonparametric Tolerance Limits PART 4 Other statistical processmonitoring and control techniques -- 9 Cumulative Sum and Exponentially Weighted Moving Average Control Charts -- Chapter Overview and Learning Objectives -- 9.1 The Cumulative Sum Control Chart -- 9.1.1 Basic Principles: The CUSUM Control Chart for Monitoring the Process Mean -- 9.1.2 The Tabular or Algorithmic CUSUM for Monitoring the Process Mean -- 9.1.3 Recommendations for CUSUM Design -- 9.1.4 The Standardized CUSUM -- 9.1.5 Improving CUSUM Responsiveness for Large Shifts -- 9.1.6 The Fast Initial Response or Headstart Feature -- 9.1.7 One-Sided CUSUMs -- 9.1.8 A CUSUM for Monitoring Process Variability -- 9.1.9 Rational Subgroups -- 9.1.10 CUSUMs for Other Sample Statistics -- 9.1.11 The V-Mask Procedure -- 9.1.12 The Self-Starting CUSUM -- 9.2 The Exponentially Weighted Moving Average Control Chart -- 9.2.1 The Exponentially Weighted Moving Average Control Chart for Monitoring the Process Mean -- 9.2.2 Design of an EWMA Control Chart -- 9.2.3 Robustness of the EWMA to Nonnormality -- 9.2.4 Rational Subgroups -- 9.2.5 Extensions of the EWMA -- 9.3 The Moving Average Control Chart -- 10 Other Univariate Statistical Process-Monitoring and Control Techniques -- Chapter Overview and Learning Objectives -- 10.1 Statistical Process Control for Short Production Runs -- 10.1.1 x and R Cha 10.11.7 Monitoring Bernoulli Processes -- 10.11.8 Nonparametric Control Charts -- 11 Multivariate Process Monitoring and Control -- Chapter Overview and Learning Objectives -- 11.1 The Multivariate Quality-Control Problem -- 11.2 Description of Multivariate Data -- 11.2.1 The Multivariate Normal Distribution -- 11.2.2 The Sample Mean Vector and Covariance Matrix -- 11.3 The Hotelling T2 Control Chart -- 11.3.1 Subgrouped Data -- 11.3.2 Individual Observations -- 11.4 The Multivariate EWMA Control Chart -- 11.5 Regression Adjustment -- 11.6 Control Charts for Monitoring Variability -- 11.7 Latent Structure Methods -- 11.7.1 Principal Components -- 11.7.2 Partial Least Squares -- 12 Engineering Process Control and SPC -- Chapter Overview and Learning Objectives -- 12.1 Process Monitoring and Process Regulation -- 12.2 Process Control by Feedback Adjustment -- 12.2.1 A Simple Adjustment Scheme: Integral Control -- 12.2.2 The Adjustment Chart -- 12.2.3 Variations of the Adjustment Chart -- 12.2.4 Other Types of Feedback Controllers -- 12.3 Combining SPC and EPC PART 5 Process design and improvement with designed experiments -- 13 Factorial and Fractional Factorial Experiments for Process Design and Improvement -- Chapter Overview and Learning Objectives -- 13.1 What is Experimental Design? -- 13.2 Examples of Designed Experiments In Process and Product Improvement -- 13.3 Guidelines for Designing Experiments -- 13.4 Factorial Experiments -- 13.4.1 An Example -- 13.4.2 Statistical Analysis -- 13.4.3 Residual Analysis -- 13.5 The 2k Factorial Design -- 13.5.1 The 22 Design -- 13.5.2 The 2k Design for k ≥ 3 Factors -- 13.5.3 A Single Replicate of the 2k Design -- 13.5.4 Addition of Center Points to the 2k Design -- 13.5.5 Blocking and Confounding in the 2k Design -- 13.6 Fractional Replication of the 2k Design -- 13.6.1 The One-Half Fraction of the 2k Design -- 13.6.2 Smaller Fractions: The 2k-p Fractional Factorial Design -- 14 Process Optimization with Designed Experiments -- Chapter Overview and Learning Objectives -- 14.1 Response Surface Methods and Designs -- 14.1.1 The Method of Steepest Ascent -- 14.1.2 Analysis of a Second-Order Response Surface -- 14.2 Process Robustness Studies -- 14.2.1 Background -- 14.2.2 The Response Surface Approach to Process Robustness Studies -- 14.3 Evolutionary Operation PART 6 Acceptance sampling -- 15 Lot-By-Lot Acceptance Sampling for Attributes -- Chapter Overview and Learning Objectives -- 15.1 The Acceptance-Sampling Problem -- 15.1.1 Advantages and Disadvantages of Sampling -- 15.1.2 Types of Sampling Plans -- 15.1.3 Lot Formation -- 15.1.4 Random Sampling -- 15.1.5 Guidelines for Using Acceptance Sampling -- 15.2 Single-Sampling Plans for Attributes -- 15.2.1 Definition of a Single-Sampling Plan -- 15.2.2 The OC Curve -- 15.2.3 Designing a Single-Sampling Plan with a Specified OC Curve -- 15.2.4 Rectifying Inspection -- 15.3 Double, Multiple, and Sequential Sampling -- 15.3.1 Double-Sampling Plans -- 15.3.2 Multiple-Sampling Plans -- 15.3.3 Sequential-Sampling Plans -- 15.4 Military Standard 105E (ANSI/ASQC Z1.4, ISO 2859) -- 15.4.1 Description of the Standard -- 15.4.2 Procedure -- 15.4.3 Discussion -- 15.5 The Dodge-Romig Sampling Plans -- 15.5.1 AOQL Plans -- 15.5.2 LTPD Plans -- 15.5.3 Estimation of Process Average -- 16 Other Acceptance-Sampling Techniques -- Chapter Overview and Learning Objectives -- 16.1 Acceptance Sampling by Variables -- 16.1.1 Advantages and Disadvantages of Variables Sampling -- 16.1.2 Types of Sampling Plans Available -- 16.1.3 Caution in the Use of Variables Sampling
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• 摘要註: This Edition continues to explore the modern practice of statistical quality control, providing comprehensive coverage of the subject from basic principles to state-of-the-art concepts and applications. The objective is to give the reader a thorough grounding in the principles of statistical quality control and a basis for applying those principles in a wide variety of both product and nonproduct situations. Divided into four parts, it contains numerous changes, including a more detailed discussion of the basic SPC problem-solving tools and two new case studies, expanded treatment on variable control charts with new examples, a chapter devoted entirely to cumulative-sum control charts and exponentially-weighted, moving-average control charts, and a new section on process improvement with designed experiments.
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