Optimization & Variation Reduction in Quality – Second Edition
$125.00 – $150.00
Covers how to optimize the average and reduce variation during product design, process design and process improvement. Provides a unified approach to engineering product and process variation.
This landmark book integrates statistical process control, design of experiments, robust design methods, Shainan’s methods and other lesser-known practices into a single system for engineering variation and optimizing performance. Professionals engaged in product design, process development, manufacturing and Six Sigma improvement activities will find this common framework has vast practical implications, not least of which is immensely improved communication between the various disciplines in the product lifecycle.
| Book Format | Hardcopy and PDF File, Hardcopy Only, PDF File Only |
|---|
Description
ISBN 0-9635122-1-8 Softcover (Replaces ISBN 0-07-063255-3 Hard Cover)
By: Dr. Wayne A. Taylor
Book Objective
Provides a unified approach to optimization and variation reduction…
from product concept to manufacturing.
Spotlighting two of today’s most effective techniques – statistical process control and robust design – this landmark book integrates these and other lesser-known practices into a single system. Professionals engaged in product design, process development, manufacturing and Six Sigma improvement activities will find this common framework has vast practical implications, not least of which is immensely improved communication between the various specializations in the product lifecycle.
There are a host of other advantages offered by this comprehensive work, including:
- An explanation of variation from the engineer’s point of view, addressing variation early in the design process to prevent problems rather than fix them later.
- An introduction to variation transmission analysis, a key method for driving optimization and variation reduction in the early phases of product design.
- Emphasis on selecting the appropriate method, with detailed discussions of Multi-Vari charts, analysis of means, component swapping, variation transmission studies, and more.
- Case studies that illustrate methods from a pragmatic, rather than theoretical, perspective.
By applying the practices and strategies set forth clearly in this book, engineers, scientist, and technical managers will look forward to dramatic improvements in product quality, product cost, and time to market.
Table of Contents
Part I: Principles and Strategies
Chapter 1: The Nature of Optimization and Variation Reduction
1.1 Objective of Book
1.2 Five Categories of Problems
1.3 Three Sources of Variation
1.4 The Goal
1.5 The Importance of Reducing Variation
1.6 Benefits
1.7 Summary
Chapter 2: Three Case Studies
2.1 Hinge Case Study
2.2 Heat Sealer Case Study
2.3 Multi-Head Filler Case Study
Chapter 3: Understanding Variation
3.1 Cause of Variation
3.2 Key Input Variables
3.3 The VIP’s
3.4 Identifying Key Inputs and VIP’s
3.5 Reducing Variation
3.6 Robustness
3.7 Myths About Variation
3.8 Summary
Chapter 4: Strategies for Optimization and Variation Reduction
4.1 Strategies for Reducing Variation
4.2 Strategies for Optimizing the Average
4.3 Objectives During Product Design
4.4 Objectives During Process Development
4.5 Objectives During Manufacturing
4.6 The Complete System
4.7 Summary
Part II: Measurement
Chapter 5: Measuring Variation
5.1 What is Variation?
5.2 Measurement of Statistical Variation
5.3 Histograms
5.4 Variation And Deviations
5.5 Measures of Central Tendency
5.6 Measures of Statistical Variation
5.7 Normal Curve
5.8 Accuracy of Estimates
5.9 Summary
5.A1 Construction of Histograms
5.A2 Calculating Standard Deviations
Chapter 6: Measuring the Three Sources of Variation
6.1 Capability Studies
6.2 Measuring Usage Variation
6.3 Measuring Variation Due to Deterioration
6.4 Summary
Chapter 7: Evaluating Measurement Systems
7.1 Properties of Good Measures
7.2 Measurement Capability Studies
7.3 Measurement Reproducibility Study
7.4 Instrument Bias Study
7.5 Attribute Versus Variable Measures
7.6 Converting Attributes to Variables
7.7 Summary
Part III: Searching for Differences
Chapter 8: Strategies for Identifying Differences
8.1 The General Strategy
8.2 Time Differences
8.3 Product Stream Differences
8.4 Unit Differences
8.5 Within Unit Differences
8.6 Summary
Chapter 9: Control Charts
9.1 Common Cause and Special Cause Variation
9.2 Uses of Control Charts
9.3 Average-Standard Deviation Control Charts
9.4 Special Patterns
9.5 Other Control Charts
9.6 Using Control Charts for Process Improvement
9.7 Using Control Charts for Process Maintenance
9.8 Summary
Chapter 10: Variation Decomposition
10.1 Multi-Vari Charts
10.2 Analysis of Means (ANOM)
10.3 Variance Components Analysis (VarComp)
10.4 Summary
CHAPTER 11: Determining Causes of Differences
11.1 General Approach
11.2 Component Swapping Studies
11.3 Assembly/Setup Studies
11.4 Unit Comparisons
11.5 Conclusion to Part III
11.6 Summary
Part IV: Studying the Inputs’ Effects
Chapter 12: Optimizing the Average
12.1 Studying the Inputs’ Effects
12.2 Identifying Key Input Variables
12.3 Scatter Diagrams
12.4 Screening Experiments
12.5 Understanding the Input/Output Relationship
12.6 Response Surface Studies
12.7 Targeting the Key Input Variables
12.8 Summary
Chapter 13: Variation Transmission Analysis
13.1 Two Approaches to Identifying VIP’s
13.2 Variation of a Polynomial
13.3 Approximating the Variation
13.4 Parameter Design
13.5 Tolerance Design
13.6 Summary
Chapter 14: The Direct Approach
14.1 Measuring Transmitted Variation Directly
14.2 The Noise Array Approach
14.3 Summary
Chapter 15: Conclusion
15.1 Conclusion to Book