Statistical Quality Design and Control

Statistical Quality Design and Control

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Description

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.show more

Product details

  • Hardback | 960 pages
  • 182 x 238 x 38mm | 1,519.55g
  • Pearson Education (US)
  • Pearson
  • Upper Saddle River, NJ, United States
  • English
  • 2nd edition
  • 0130413445
  • 9780130413444
  • 2,033,685

Table of contents

PART I: FUNDAMENTAL CONEPTS AND METHODSChapter 1 Evolution of Quality Design and ControlChapter 2 Conceptual Framework for Quality: Design and ControlChapter 3 Statistical Methods and Probability Concepts for Data CharacterizationChapter 4 Sampling Distributions and Statistical Hypothesis TestingChapter 5 Conceptual Framework for Statistical Process Control PART II: PROCESS CONTROL AND IMPROVEMENTChapter 6 Shewhart Control Charts for Variable DataChapter 7 Importance of Rational SamplingChapter 8 Interpretation of X and R Control Charts: Use of Sampling ExperimentsChapter 9 Some Control Chart Methods for Individual MeasurementsChapter 10 Process Capability AssessmentChapter 11 The Design and Analysis of TolerancesChapter 12 Statistical Thinking for Process Study: A Case StudyChapter 13 Shewhart Control Charts for Attribute DataChapter 14 Attribute Control Chart Implementation for Process Improvement: Two Case StudiesPART III: PRODUCT/PROCESS DESIGN AND IMPROVEMENTChapter 15 Conceptual Framework for Planned ExperimentationChapter 16 Design and Analysis of Simple Comparative ExperimentsChapter 17 Design and Interpretation of 2k Factorial ExperimentsChapter 18 Analysis of Two-Level Factorial DesignsChapter 19 Model Building for Design and Improvement Using Two-Level Factorial DesignsChapter 20 Two-Level Fractional Factorial DesignsChapter 21 Sequential and Iterative Nature of ExperimentationChapter 22 Robust Design Case StudiesChapter 23 Modeling of Response Surfaces and Response OptimizationReferences and Further ReadingsAppendix Tablesshow more

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1 10% (1)
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