Statistical quality control / Eugene L. Grant, Richard S. Leavenworth.

Incluye diskette, nº inv. RE0284

CONTENIDO
1 Introduction and Overview
1.1 The Meaning of Quality
1.2 The Focus of This Book
1.3 The Control-Chart Viewpoint
1.4 Scientific Sampling
1.5 Use of Examples
1.6 Meanings and Usage of the Words Defective and Defect
1.7 Many Economy Studies Call for the Viewpoint of Statistical Quality Control
1.8 Statistical Quality Control May Have Useful By-Products
1.9 Reasons for the Use of the Adjective Statistical
1.10 Four Different Levels of Understanding Statistical Quality Control
1.11 Nonmanufacturing Applications of Statistical Quality Control Techniques
1.12 Some Topics Covered in Part Four Of This Book
PART TWO STATISTICAL PROCESS CONTROL
2 Directions for Simple X and R Charts
2.1 Setting Up and Operating Control Charts for X and R
2.2 Checklist of Necessary Steps in Using X and R Charts
2.3 Some Comments on Computer Software for Statistical Process Control
3 Why the Control Chart Works; Some Statistical Concepts
3.1 The Need for Understanding Statistical Principles
3.2 Description of Patterns of Variation
3.3 Graphic Representation of a Frequency Distribution
3.4 Averages and Measures of Dispersion
3.5 Sampling Statistics and Universe Parameters
3.6 The Normal Curve
3.7 Other Frequency Curves
3.8 What the Average and Standard Deviation of a Set of Numbers Really Tell
4 Why the Control Chart Works; Some Examples
4.1 The Use of Control Charts to Judge Whether or Not a Constant System of Chance Causes Is Present
4.2 Use of the Control Chart in Interpretation of a Frequency Distribution
4.3 Contribution of the Control Chart to Elimination of Causes of Trouble
4.4 Changes in Universe Average
4.5 Shift in Universe Dispersion with No Change in Universe Average
4.6 Changes in Universe Average and Universe Dispersion
4.7 A Possible View of the Question Answered by a Control Chart
4.8 Nonproduct Applications Of Control Charts for Variables
4.9 Conflicting Expressions for the Standard Deviation of a Set of Numbers
5 Some Fundamentals of the Theory of Probability
5.1 Probability Has a Mathematical Meaning
5.2 Modern Concepts of Probability Theory
5.3 Some Theorems of the Theory of Probability
5.4 Infinite and Finite Universes
5.5 The Hypergeometric Probability Distribution
5.6 The Binomial as a Probability Distribution
5.7 The Poisson Law as a Probability Distribution
5.8 The Normal Distribution
5.9 Deciding on the Method to Be Used for Calculating Probabilities in Industrial Sampling
5.10 Relationship between Control Charts and Certain Other Statistical Techniques
5.11 Random Variables
5.12 Point Estimates and Estimators
5.13 The Problem of Selecting a Parameter to Describe Universe Dispersion and of Choosing an Estimator for That Parameter
5.14 Sampling from a Normal Distribution
5.15 Theory of Extreme Runs
Problems
6 The Control Chart for Fraction Rejected
6.1 Some Practical Limitations of Control Charts for Variables
6.2 Control Charts for Attributes
6.3 The Control Chart for Fraction Rejected
6.4 The Binomial as a Probability Law That Determines the Fluctuations of Fraction Rejected
6.5 Control Limits for the p Chart
6.6 Problems Introduced by Variable Subgroup Size
6.7 Checklist of Necessary Steps in Connection with Control Chart for Fraction Rejected
6.8 Sensitivity of the p Chart
6.9 Nonproduct Applications Of p and up Charts
6.10 p Charts Are Not Suitable for All Data on Fraction Rejected
Problems
7 The Control Chart for Nonconformities
7.1 The Place of the c Chart in Statistical Process Control
7.2 Distinction between a Nonconforming Article and a Nonconformity
7.3 Limits for the c Chart Are Based on the Poisson Distribution
7.4 The Combination of Poisson Distributions
7.5 Conditions Favorable to the Economic Use of the Control Chart for Nonconformities
7.6 Adaptations Of the c Chart to Variations in 1he Area of Opportunity for a Nonconformity
7.7 Probability Limits for c and u Charts
7.8 The u Chart for Nonconformities per Multiple Units
7.9 Listing Individual Nonconformities on the Form Containing a c or u Chart
7.10 The Introduction of a Control Chart May Motivate Quality Improvement
7.11 Classification of Nonconformities and Their Weighting
7.12 Q Charts for Quality Scores and D Charts for Demerit Classifications
7.13 Use of 3 c for Approximate Calculation of Control Limits in Situations Involving the Binomial Distribution
7.14 Applicability Of c Chart Technique in Fields Other Than Statistical Process Control
Problems
8 Rational Subgrouping
8.1 The Information Given by the Control Chart Depends on the Basis Used for Selection of Subgroups
8.2 Two Schemes Involving Order of Production as a Basis for Subgrouping
8.3 Question Addressed by the Shewhart Control Chart
8.4 Sources of Variability
8.5 Order of Production Is Not Always a Sufficient Basis for Subgrouping
8.6 Need for Discrimination in the Selection of Subgroups
8.7 Identification on a Control Chart of Different Sources of Subgroups
8.8 Precision, Reproducibility and Accuracy of Methods of Measurement
8.9 Relationship between the Variability of Measured Values and the Precision of the Method of Measurement
Problems
9 Statistical Analysis of Process Capability and for Process Improvement
9.1 Process Capability as a Step toward Process Improvement
9.2 Process Capability and Performance Indexes
9.3 Quality by Design: Design and Inspection Specifications
9.4 Statistical Methods May Help in Setting Better Specification Limits
9.5 Some Common Methods of Interpretation Of a Pilot Run as a Basis for Setting Tolerances
9.6 Two Statistical Theorems of Great Importance in the Interrelationship of Tolerances
9.7 Experimentation
Problems
10 Some Special Process Control Procedures
10.1 Some Miscellaneous Topics
10.2 Some Special Topics on Shewhart Control Charts for Variables
10.3 Some Related Special Procedures
10.4 A General Test for Homogeneity
10.5 Probability Limits on Control Charts for Variables
10.6 Control Charts for Moving Averages
10.7 X Chart with a Linear Trend
10.8 Narrow Limit Gaging
10.9 Working with Short Production Runs
10.10 A Success Chart for Production Runs of Extremely High Quality
10.11 Combining Process Control and Product Acceptance
10.12 Cumulative Sum Control Chart for Averages
Problems
PART THREE SCIENTIFIC SAMPLING
11 Some Fundamental Concepts in Scientific Sampling
11.1 The Importance of Sampling
11.2 Some Weaknesses of Certain Traditional Practices in Acceptance Sampling
11.3 Purpose of This Chapter
11.4 Lot-by-Lot Acceptance Using Single Sampling by Attributes
11.5 OC Curve of an Ideal Sampling Plan
11.6 The Indexing of Acceptance Plans by a Single Point on the OC Curve
11.7 Average Outgoing Quality and the AOQL
11.8 Double Sampling
11.9 Choosing a Sampling Plan to Minimize Average Total Inspection
11.10 Multiple and Sequential Sampling
11.11 Randomness in Acceptance Sampling
Problems
12 An AQL System for Lot-by-Lot Acceptance Sampling by Attributes
12.1 Selecting an Acceptance Inspection Procedure
12.2 A Historical Note Regarding Acceptance Sampling Systems Based on the AQL Concept
12.3 Some Decisions Made in the Original Establishment of the AQL as a Quality Standard
12.4 Some Aspects of the Master Tables Reproduced from the ABC Standard
12.5 Determining the Sample Size Code Letter
12.6 OC Curves under Normal, Tightened. and Reduced Inspection
12.7 Single, Double, and Multiple Sampling Plans in AQL Systems
12.8 Classification of Defects
12.9 The Formation of Inspection Lots
12.10 Acceptance Based on Numbers of Defects
12.11 A Systematic Record of Quality History Is an Important Aspect of Statistical Acceptance Procedures
12.12 Selecting an Acceptance Plan for an Isolated Lot
12.13 Importance of AOQL Values in Sampling Plans Based on the AQL
Problems
13 Other Procedures for Acceptance Sampling by Attributes
13.1 Two Useful Volumes of Standard Tables
13.2 The Dodge-Romig Tables
13.3 Some Reasons for Not Basing a Quality Standard on a Provision for Screening Inspection
13.4 Designing Single Sampling Plans for Stipulated Producer's and Consumer's Risks
13.5 A Simple AQL System Proposed by Dodge
13.6 Design Of a Sequential Plan Having an OC Curve Passing through Two Designated Points
13.7 Dodge's Chain Sampling Inspection Plan
Problems
14 Systems for Acceptance Sampling from Continuous Production
14.1 Dodge's AOQL Plan for Continuous Production-CSP-1
14.2 Multilevel Continuous Sampling Plans (CSP-M)
14.3 The MIL-STD-1235 System for Sampling from Continuous Production
14.4 AOQ Functions of Some Continuous Sampling Schemes
14.5 Skip-Lot Sampling
14.6 Further Comment on Continuous Sampling Plans
14.7 Unique Features of CSP Plans as Guides for SPC Sampling
Problems
15 Systems for Acceptance Sampling by Variables
15.1 Some Advantages and Limitations of Acceptance Sampling by Variables
15.2 Some Different Types of Acceptance Criteria Involving Variables
15.3 Using Plotted Frequency Distributions in Acceptance Sampling
15.4 Use of Control Charts to Identify Grand Lots
15.5 Computing the OC Curve for a Known-Sigma Variables Sampling Plan Based on the Assumption of a Normal Distribution
15.6 Some General Aspects of Military Standard 414
15.7 Numerical Data to Illustrate Normal Inspection under MIL-STD-414
15.8 Tightened Inspection in MIL-STD-414 l 15.9 Reduced Inspection in MIL-STD-414
15.10 An Upper Specification Limit in MIL-STD-414
15.11 Two-Sided Specifications in MlL-STD-414
15.12 The Relationship among Sample Sizes under the Standard Deviation Method, the Range Method, and the Procedures Assuming Known Variability
15.13 Some Comments on Table 15.4
15.14 The Choice between Unknown-Sigma and Known-Sigma Plans
15.15 Some Sources of OC Curves for Variables Plans Based on the Assumption of Normality
15.16 Comment on the Assumption of a Normal Distribution in Known-Sigma and Unknown-Sigma Plans
15.17 Acceptance/Rejection Plans May Be Devised to Accept on Variables Criteria but to Reject Only on Attributes Criteria
15.18 International and Commercial Standards Corresponding to MIL-STD-414
Problems
16 Some Aspects of Life Testing and Reliability
16.1 Purpose of This Chapter
16.2 A Conventional Model of the Probability of Equipment Failure
16.3 Some Modem Definitions of Reliability
16.4 The Relationship between a Constant Failure Rate and Mean Life or Mean Time between Failures
16.5 An Experiment to Illustrate Certain Aspects of a Constant Failure Rate
16.6 The "Exponential" Reliability Function That Results from the Assumption of a Constant Failure Rate
16.7 Principal U.S. Government Documents That Treat Life Testing under the Assumption of a Constant Failure Rate
16.8 The Broad General Usefulness of Sampling and Tasting Standards
Problems
PART FOUR SOME RELATED TOPICS
17 Some Economic Aspects of Quality Decisions
17.1 Problems of Business Alternatives Are Problems in Economy
17.2 Some Basic Concepts in Engineering Economy
17.3 Three General Classes Of Consequences That Should Be Recognized in Making Certain Quality Decisions
17.4 Taguchi's Loss Function
17.5 Some Economic Aspects of the Margin of Safety in Design Specifications
17.6 Some Special Difficulties of Estimating Indirect Costs in Economy Studies
17.7 Did It Pay to Use Statistical Quality Control?
17.8 The Increasing Importance of Costs Related to Product Liability
Problems
18 Some Significant Events in the Development of Statistical Quality Control
18.1 Quality and Standardization
18.2 The Two Architects of Statistical Quality Control
18.3 Quality Assurance Science and World War II
18.4 Japan's Recovery as a WorldClass Industrial Power
18.5 The Quality Cultural Revolution in the United States
18.6 U.S. Awards for Quality and Productivity Improvement
18.7 Quality System Standards and Standardization
18.8 An Apology to Those Not Mentioned
19 Models for Quality Management and Problem Solving
19.1 Evolution of the "Quality Wheel"
19.2 Names Used to Describe the Quality Management Movement
19.3 The Baldrige Criteria as a Quality Management Model
19.4 Comparing the Baldrige Criteria and ISO 9000 Requirements
19.5 Problem-Solving Models Compared to Management Models
19.6 The "Seven Basic Tools of Statistical Process Control"
19.7 Tracking and Celebrating Quality
20 Demonstrating the Operation of Systems Of Chance Causes
20.1 Use of Group Experiments in Introducing the Subjects of Control Charts and Acceptance Sampling Procedures
20.2 Demonstrating Frequency Distributions and Control Charts for Variables
20.3 Demonstrating Acceptance Sampling by Attributes
20.4 Deming's Red Bead Experiment
APPENDIXES
1 Glossary of Symbols
2 Bibliography
3 Tables
NAME INDEX
SUBJECT INDEX