Statistical Quality Design and Control : Contemporary Concepts and Methods

by DeVor, Richard T.; Tsong-how, Chang

• ISBN13: 9780023291807
• ISBN10: 002329180X
• Format: Paperback
• Copyright: 1992-01-01
• Publisher: Prentice Hall

Summary

A textbook that can be used for a course or sequences of courses on quality design and improvement in both schools of engineering and management. DLC: Quality control-Statistical methods.

Table of Contents

PART I Fundamental Concepts and Methods
Evolution of Quality Design and Control	3	(30)
Introduction	3	(1)
Quality Revolution of the 1980s	4	(3)
Historical Perspective of the Management of Quality	7	(7)
Quality and the Engineering Design Process	14	(5)
Strategic View of Quality Design and Improvement	19	(6)
Contrasting Approaches to Quality Design and Improvement	25	(8)
Exercises	31	(2)
Conceptual Framework for Quality: Design and Control	33	(25)
Introduction	33	(1)
Deming Philosophy of Never-Ending Improvement	34	(9)
Traditional View of Quality	43	(4)
Taguchi's Definition of Quality	47	(4)
Cost-of-Quality Issue	51	(7)
Exercises	56	(2)
Statistical Methods and Probability Concepts for Data Characterization	58	(53)
Introduction	58	(1)
Purpose and Nature of Sampling	58	(5)
Characterizations and Representations of Data	63	(6)
Some Important Concepts of Probability and Probability Distributions	69	(7)
Normal Distribution	76	(4)
Sampling Distribution of X	80	(5)
Some Other Useful Graphical Representations of Data	85	(26)
Exercises	99	(12)
PART II Process Control and Improvement
Conceptual Framework for Statistical Process Control	111	(27)
Introduction	111	(1)
Origins and Characteristic Behavior of Variability	112	(4)
Process Behavior over Time	116	(6)
Shewhart's Concept of Statistical Control	122	(4)
Control Chart Approach to the Management of Process Variation	126	(6)
The Process of Statistical Process Control	132	(6)
Exercises	136	(2)
Statistical Basis for Shewhart Control Charts for Variable Data	138	(15)
Introduction	138	(1)
Concept of Hypothesis Testing	138	(7)
Shewhart Control Chart Model	145	(2)
Control Chart for Averages, X	147	(3)
Control Chart for Ranges	150	(3)
Construction and Interpretation of Shewhart Control Charts for Variable Data	153	(44)
Introduction	153	(1)
Setting Up X and R Control Charts	154	(3)
Interpretation of Shewhart Control Chart Patterns	157	(7)
Example: Construction of X and R Control Charts	164	(7)
Closing the Loop Using the Process of SPC	171	(26)
Exercises	179	(18)
Importance of Rational Sampling	197	(22)
Introduction	197	(1)
Concept of Rational Sampling	197	(3)
Merits of Consecutive Versus Distributed Sampling	200	(3)
Some Common Pitfalls in Subgroup Selection	203	(4)
Examples of the Stratification Phenomenon	207	(12)
Exercises	215	(4)
Interpretation of X and R Control Charts: Use of Sampling Experiments	219	(36)
Introduction	219	(1)
Analysis of Control Chart Patterns: A Sampling Experiment	220	(15)
Illustration of Stratification of Subgroups	235	(20)
Computer Workshop 1	247	(8)
Process Capability Assessment	255	(43)
Introduction	255	(1)
Process Capability Versus Process Control	256	(2)
Statistical Assessment of Process Capability: A Case Study Revisited	258	(6)
Some Common Indices of Process Capability	264	(4)
Statistical Assessment and Assignment of Tolerances	268	(7)
Statistical Process Control and the Statistical Tolerance Model	275	(4)
Loss Function Approach to Capability Assessment	279	(19)
Exercises	287	(11)
Statistical Thinking for Process Study: A Case Study	298	(48)
Introduction	298	(1)
Overview of the Case Study	299	(2)
Product Versus Process Control: Management Strategy	301	(8)
Finding Improvement Opportunities Through Charting	309	(12)
Process Capability Versus Process Control	321	(8)
Seeking Further Improvement Through Design of Experiments	329	(17)
Computer Workshop 2	338	(5)
Exercises	343	(3)
Some Control Chart Methods for Individual Measurements	346	(38)
Introduction	346	(1)
X and Rm Control Charts	347	(2)
Case Study: White Millbase Dispersion Process	349	(12)
Exponentially Weighted Moving Average Control Charts	361	(23)
Exercises	372	(12)
Cumulative-Sum Control Charts	384	(42)
Introduction	384	(1)
Traditional Cusum Control Charts	385	(6)
Discussion of Traditional Cusum Control Charts	391	(3)
Interpretation of Cusum Control Charts	394	(1)
Shewhart-Like Cusum Control Charts	395	(10)
Cusum Plots for Data Analysis	405	(21)
Exercises	416	(10)
Shewhart Control Charts for Attribute Data	426	(55)
Data Characterization by Attributes	426	(4)
Control Chart for Fraction Defective	430	(13)
Variable-Sample-Size Considerations for the p Chart	443	(6)
Control Chart for Number of Defectives	449	(3)
Control Chart for Number of Defects	452	(6)
Control Chart for Number of Defects per Unit	458	(5)
Exercises	463	(18)
Attribute Control Chart Implementation for Process Improvement: Two Case Studies	481	(22)
Introduction	481	(1)
Implementation of SPC Methods: A Case Study on ``Press 120''---Visual Defects in a Molding Process	482	(9)
Improvement of an Accounts Payable Process	491	(12)
PART III Product/Process Design and Improvement
Conceptual Framework for Planned Experimentation	503	(39)
Introduction	503	(1)
Role of Experimentation in Quality Design and Improvement	504	(2)
Some Important Issues in Planned Experimentation	506	(10)
Dealing with Noise in the Experimental Environment	516	(8)
Role of Planned Experimentation in Taguchi Methods	524	(11)
Contrasting Philosophies for Dealing with Nuisance or Noise Factors	535	(7)
Exercises	537	(5)
Design and Interpretation of 2k Factorial Experiments	542	(43)
Introduction	542	(1)
Process Study Using a Two-Level Factorial Design: Chemical Calibration for the Foam Molding Process	543	(3)
Construction of the 23 Factorial Design	546	(2)
Calculation and Interpretation of Main Effects	548	(7)
Calculation and Interpretation of Interaction Effects	555	(9)
Generalized Method for the Calculation of Effects	564	(3)
Mathematical Model of the Response	567	(18)
Exercises	572	(13)
Analysis of Two-Level Factorial Designs	585	(28)
Introduction	585	(1)
Analysis of Replicated 2k Experiments	586	(7)
Determination of Statistically Significant Effects	593	(7)
Analysis of the Results of Unreplicated Two-Level Factorials	600	(13)
Exercises	606	(7)
Model Building for Design and Improvement Using Two-Level Factorial Designs	613	(61)
Introduction	613	(1)
Glove Box Door Alignment Study Revisited	614	(5)
Diagnostic Checking of the Fitted Model	619	(8)
Checking the Assumption of Common Variance of the Responses	627	(4)
Using the Fitted Model for Quality Improvement	631	(4)
Another Case Study: Surface Finish of a Machined Part	635	(9)
Simple Decomposition of the Variation in Data from an Experiment	644	(2)
Formalization of the Analysis of Variance Method	646	(5)
Use of Analysis of Variance for Two-Level Factorial Design Effect Evaluation	651	(6)
Use of Analysis of Variance on the Glove Box Door Alignment Study	657	(17)
Exercises	661	(13)
Two-Level Fractional Factorial Designs	674	(46)
Rationale for, and Consequences of, Fractionation of Two-Level Factorials	674	(4)
System to Define the Confounding Pattern of a Two-Level Fractional Factorial	678	(7)
Procedure for Design Characterization: Another Example	685	(3)
Concept of Resolution of Two-Level Fractional Factorial Designs	688	(3)
Case Study Application: The Sandmill Experiment Revisited	691	(7)
Orthogonal Arrays and Two-Level Fractional Factorial Designs	698	(22)
Exercises	707	(13)
Sequential and Iterative Nature of Experimentation	720	(34)
Value of Sequential Experimentation: A Case Study	720	(13)
Sequential Assembly of Fractional Factorials	733	(8)
Soybean Whipped Topping Example: Summary and Interpretation	741	(13)
Exercises	744	(10)
Robust Design Case Studies	754	(26)
Taguchi's Approach to Robust Design	754	(1)
Overview of Approaches to Robust Design	755	(1)
Case Study 1: Fuel Gage Calibration	756	(5)
Case Study 2: Glove Box Closing Effort Analysis	761	(6)
Case Study 3: Automobile Door Closing Effort	767	(2)
Case Study 4: Simultaneous Engineering of a Product and Its Manufacturing Process	769	(11)
References and Further Readings	780	(7)
Appendix Tables	787	(12)
Index	799