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Statistical Quality Design and  Control,9780130413444
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Statistical Quality Design and Control

by ; ;
Edition:
2nd
ISBN13:

9780130413444

ISBN10:
0130413445
Format:
Paperback
Pub. Date:
7/25/2006
Publisher(s):
Prentice Hall
List Price: $177.00

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This is the 2nd edition with a publication date of 7/25/2006.
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Summary

Features a blend of statistical process control (SPC) and design of experiments (DOE) concepts and methods for quality design and improvement. The book places particularly strong emphasis on proper methods for data collection, control chart construction and interpretation, and fault diagnosis for process improvement.

Table of Contents

Part I Fundamental Concepts and Methods
1(134)
Evolution of Quality Design and Control
3(31)
Introduction
3(1)
Quality Revolution of the 1980s
4(2)
Historical Perspective of the Management of Quality
6(7)
Quality and the Engineering Design Process
13(4)
Strategic View of Quality Design and Improvement
17(9)
Beyond the 1980s---The Institutionalization of Quality in the Organization
26(8)
Chapter Summary
31(1)
Exercises
31(3)
Conceptual Framework for Quality: Design and Control
34(30)
Introduction
34(1)
Deming Philosophy of Never-Ending Improvement
35(14)
Traditional View of Quality
49(3)
Taguchi's Definition of Quality
52(5)
Cost-of-Quality Issue
57(7)
Exercises
62(2)
Statistical Methods and Probability Concepts for Data Characterization
64(42)
Introduction
64(1)
Characterizations and Representations of Data
64(7)
Some Important Concepts of Probability and Probability Distributions
71(7)
Normal Distribution
78(4)
Some Other Useful Graphical Representations of Data
82(24)
Exercises
95(11)
Sampling Distributions and Statistical Hypothesis Testing
106(29)
Introduction
106(1)
Purpose and Nature of Sampling
107(3)
Sampling Distribution of X
110(5)
Hypothesis Testing of a Population Mean
115(11)
Confidence Interval Estimation of the mean
126(9)
Exercises
130(5)
Part II Process Control and Improvement
135(388)
Conceptual Framework for Statistical Process Control
137(38)
Introduction
137(1)
Origins and Characteristic Behavior of Variability
138(4)
Process Behavior Over Time
142(5)
Shewhart's Concept of Statistical Control
147(4)
Control Chart Approach to the Management of Process Variation
151(7)
The Process of Statistical Process Control
158(3)
Deming's Famous Red Bead Experiment
161(9)
The Debilitating Effects of Tampering
170(5)
Exercises
172(3)
Shewhart Control Charts for Variable Data
175(62)
Introduction
175(1)
Statistical Basis for Shewhart Control Charts
175(3)
Control Chart for Averages, X
178(3)
Control Chart for Ranges
181(2)
Setting Up X and R Control Charts
183(2)
Interpretation of Shewhart Control Chart Patterns
185(8)
Closing the Loop Using the Process of SPC
193(7)
Cylinder Liner Boring Case Study---Demonstrating the Process of SPC
200(12)
A Note on Computer Software
212(25)
Exercises
213(24)
Importance of Rational Sampling
237(23)
Introduction
237(1)
Concept of Rational Sampling
237(3)
Merits of Consecutive Versus Distributed Sampling
240(1)
Some Common Pitfalls in Subgroup Selection
241(6)
Examples of the Stratification Phenomenon
247(13)
Exercises
254(6)
Interpretation of X and R Control Charts: Use of Sampling Experiments
260(29)
Introduction
260(1)
Analysis of Control Chart Patterns: A Sampling Experiment
261(13)
Illustration of Stratification of Subgroups
274(15)
SPC Workshop #1: Process Simulation, PROSIM
287(2)
Some Control Chart Methods for Individual Measurements
289(38)
Introduction
289(1)
X and Rm Control Charts
290(2)
Case Study: White Millbase Dispersion Process
292(9)
Exponentially Weighted Moving Average Control Charts
301(26)
Exercises
313(14)
Process Capability Assessment
327(39)
Introduction
327(1)
Process Capability Versus Process Control
327(3)
Statistical Assessment of Process Capability: A Case Study Revisited
330(6)
Some Common Indices of Process Capability
336(3)
Loss Function Approach to Capability Assessment
339(9)
The Dilemma of Cpk: A Case Study
348(18)
Exercises
357(9)
The Design and Analysis of Tolerances
366(38)
Introduction
366(1)
Tolerancing and Competitiveness
367(5)
Statistical Interpretation of Six Sigma Quality
372(5)
Tolerance Design and Analysis
377(6)
Statistical Assessment and Assignment of Tolerances
383(8)
Statistical Process Control and the Statistical Tolerance Model
391(13)
Exercises
395(9)
Statistical Thinking for Process Study: A Case Study
404(41)
Introduction
404(1)
Overview of the Case Study
405(2)
Product Versus Process Control: Management Strategy
407(7)
Finding Improvement Opportunities Through Charting
414(10)
Process Capability Versus Process Control
424(8)
Seeking Further Improvement Through Design of Experiments
432(13)
Chapter Summary
440(1)
Exercises
441(2)
SPC Workshop #2: Turning Simulation, TURNSIM
443(2)
Shewhart Control Charts for Attribute Data
445(58)
Data Characterization by Attributes
445(4)
Control Chart for Fraction Defective
449(14)
Variable-Sample-Size Considerations for the p Chart
463(6)
Control Chart for Number of Defectives
469(2)
Control Chart for Number of Defects
471(8)
Control Chart for Number of Defects per Unit
479(24)
Exercises
483(20)
Attribute Control Chart Implementation for Process Improvement: Two Case Studies
503(20)
Introduction
503(1)
Implementation of SPC Methods: A Case Study on ``Press 120''---Visual Defects in a Molding Process
503(9)
Improvement of an Accounts Payable Process
512(11)
Part III Product/Process Design and Imoprovement
523(383)
Conceptual Framework for Planned Experimentation
525(38)
Introduction
525(1)
Role of Experimentation in Quality Design and Improvement
525(3)
Some Important Issues in Planned Experimentation
528(9)
Dealing with Noise in the Experimental Environment
537(8)
Role of Planned Experimentation in Taguchi Methods
545(11)
Contrasting Philosophies for Dealing with Nuisance or Noise Factors
556(7)
Exercises
558(5)
Design and Analysis of Simple Comparative Experiments
563(50)
Introduction
563(1)
Comparing Two Alternatives Using an External Reference Distribution--Car Mileage Example
563(4)
Comparing Two Alternatives via an Internal Reference Distribution
567(6)
Comparing Two Alternatives Using a Blocked Design
573(7)
Comparison of k Treatments Using Analysis of Variance---One-Way ANOVA
580(12)
Comparison of Multiple Treatments Using a Blocked Design---Two-Way ANOVA
592(21)
Chapter Summary
602(1)
Exercises
603(10)
Design and Interpretation of 2k Factorial Experiments
613(52)
Introduction
613(1)
Process Study Using a Two-Level Factorial Design: Chemical Calibration for the Foam Molding Process
614(2)
Construction of the 23 Factorial Design
616(2)
Calculation and Interpretation of Main Effects
618(6)
Calculation and Interpretation of Interaction Effects
624(9)
Generalized Method for the Calculation of Effects
633(2)
Mathematical Model of the Response
635(6)
Least-Squares Approach to Model Fitting
641(24)
Exercises
651(14)
Analysis of Two-Level Factorial Designs
665(30)
Introduction
665(1)
Analysis of Replicated 2k Experiments
666(7)
Determination of Statistically Significant Effects
673(7)
Analysis of the Results of Unreplicated Two-Level Factorials
680(15)
Chapter Summary
684(1)
Exercises
685(10)
Model Building for Design and Improvement Using Two-Level Factorial Designs
695(56)
Introduction
695(1)
Glove Box Door Alignment Study Revisited
696(5)
Diagnostic Checking of the Fitted Model
701(8)
Checking the Assumption of Common Variance of the Responses
709(3)
Using the Fitted Model for Quality Improvement
712(4)
Another Case Study: Surface Finish of a Machined Part
716(8)
Use of Analysis of Variance for Two-Level Factorial Design Effect Evaluation
724(7)
Use of Analysis of Variance on the Glove Box Door Alignment Study
731(20)
Exercises
735(14)
DOE Workshop #1: Turning Simulation, TURN5DOE
749(2)
Two-Level Fractional Factorial Designs
751(46)
Rationale for, and Consequences of, Fractionation of Two-Level Factorials
751(4)
System to Define the Confounding Pattern of a Two-Level Fractional Factorial
755(7)
Procedure for Design Characterization: Another Example
762(3)
Concept of Resolution of Two-Level Fractional Factorial Designs
765(3)
Case Study Application: The Sandmill Experiment Revisited
768(7)
Orthogonal Arrays and Two-Level Fractional Factorial Designs
775(22)
Exercises
784(13)
Sequential and Iterative Nature of Experimentation
797(35)
Value of Sequential Experimentation: A Case Study
797(13)
Sequential Assembly of Fractional Factorials
810(8)
Soybean Whipped-Topping Example: Summary and Interpretation
818(14)
Exercises
821(9)
DOE Workshop #2: Turning Simulation, TURN9DOE
830(2)
Robust Design Case Studies
832(26)
Taguchi's Approach to Robust Design
832(1)
Overview of Approaches to Robust Design
832(2)
Case Study 1: Fuel Gage Calibration
834(5)
Case Study 2: Glove Box Closing Effort Analysis
839(5)
Case Study 3: Automobile Door Closing Effort
844(2)
Case Study 4: Simultaneous Engineering of a Product and Its Manufacturing Process
846(12)
Modeling of Response Surfaces and Response Optimization
858(48)
Introduction
858(2)
Modeling the Response Surface
860(1)
Response Surface Methodology---A Brief Description
861(7)
RSM Example-A Pulp Whitening Process
868(13)
Soldering Process Optimization---A Case Study
881(25)
Exercises
895(9)
DOE Workshop #3: Response Surface Methodology, RSMGAME
904(2)
References and Further Readings 906(9)
Appendix Tables 915(12)
Index 927


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