Statistical Quality Design and Control: Contemporary Concepts and Methods, 2nd edition

Published by Pearson (July 25, 2006) © 2007

  • Richard E. DeVor University of Illinois, Urbana
  • John W. Sutherland Michigan Technological University
  • Tsong-how Chang
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Title overview

Emphasizing proper methods for data collection, control chart construction and interpretation, and fault diagnosis for process improvement, this text blends statistical process control (SPC) and design of experiments (DOE) concepts and methods for quality design and improvement.

 

Importance is placed on both the philosophical/conceptual underpinnings and the techniques and methods of SPC and DOE. The concepts and methods of Taguchi for quality design are combined with more traditional experimental design methods to promote the importance of viewing quality from an engineering design perspective.

Table of contents

PART I: FUNDAMENTAL CONEPTS AND METHODS

Chapter 1 Evolution of Quality Design and Control

Chapter 2 Conceptual Framework for Quality: Design and Control

Chapter 3 Statistical Methods and Probability Concepts

for Data Characterization

Chapter 4 Sampling Distributions and Statistical Hypothesis Testing

Chapter 5 Conceptual Framework for Statistical Process Control

 

PART II: PROCESS CONTROL AND IMPROVEMENT

Chapter 6 Shewhart Control Charts for Variable Data

Chapter 7 Importance of Rational Sampling

Chapter 8 Interpretation of X and R Control Charts:

Use of Sampling Experiments

Chapter 9 Some Control Chart Methods for Individual Measurements

Chapter 10    Process Capability Assessment

Chapter 11    The Design and Analysis of Tolerances

Chapter 12    Statistical Thinking for Process Study: A Case Study

Chapter 13    Shewhart Control Charts for Attribute Data

Chapter 14    Attribute Control Chart Implementation

for Process Improvement: Two Case Studies

PART III: PRODUCT/PROCESS DESIGN AND IMPROVEMENT

Chapter 15    Conceptual Framework for Planned Experimentation

Chapter 16    Design and Analysis of Simple Comparative Experiments

Chapter 17    Design and Interpretation of 2k Factorial Experiments

Chapter 18    Analysis of Two-Level Factorial Designs

Chapter 19    Model Building for Design and Improvement

Using Two-Level Factorial Designs

Chapter 20    Two-Level Fractional Factorial Designs

Chapter 21    Sequential and Iterative Nature of Experimentation

Chapter 22    Robust Design Case Studies

Chapter 23    Modeling of Response Surfaces and Response Optimization

References and Further Readings

Appendix Tables

 

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