Quality Engineering: Off-Line Methods and Applications, 1st Edition (Hardback) book cover

Quality Engineering

Off-Line Methods and Applications, 1st Edition

By Chao-Ton Su

CRC Press

394 pages | 140 B/W Illus.

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pub: 2013-02-26
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As quality becomes an increasingly essential factor for achieving business success, building quality improvement into all stages—product planning, product design, and process design—instead of just manufacturing has also become essential. Quality Engineering: Off-Line Methods and Applications explores how to use quality engineering methods and other modern techniques to ensure design optimization at every stage. The book takes a broad approach, focusing on the user’s perspective and building a well-structured framework for the study and implementation of quality engineering.

Starting with the basics, this book presents an overall picture of quality engineering. The author delineates quality engineering methods such as DOE, Taguchi, and RSM as well as computational intelligence approaches. He discusses how to use a general computational intelligence approach to improve product quality and process performance. He also provides extensive examples and case studies, numerous exercises, and a glossary of basic terms.

By adopting quality engineering, the defect rate during manufacturing shows noticeable improvement, the production cost is significantly lower, and the quality and reliability of products can be enhanced. Taking an integrated approach that makes the methods of upstream quality improvement accessible, without extensive mathematical treatments, this book is both a practical reference and an excellent textbook.


"This book provides a high quality reference for all engineers who wish to apply experimental design to actual product and process designs. It is well-organized and provides numerous practical examples and case studies that help the reader understand factorial experimental techniques, Taguchi Methods and other modern techniques more easily."

—Gregory H. Watson,Past-President of ASQ and Past-Chairman of IAQ Chairman, Business Excellence Solutions, Ltd.

"During my thirty years in the semiconductor industry I have witnessed quality engineering methods become widely applied to shorten R&D cycle time, optimize product/process parameters, and save cost. This book provides the complete structure of quality engineering with plenty of practical cases. It will help readers to learn these methods quickly and contribute to business success."

—Long-Chin Tu, Vice President, Taiwan Semiconductor Manufacturing Company

"Quality engineering methods are commonly used in industry to upgrade the quality level. This book is well written and is of great interest both to students and professionals wishing to develop or expand their knowledge of quality engineering. It contains clear presentation and practical implementation that are often missing from other texts."

—Fugee Tsung, Hong Kong University of Science and Technology

"Unimicron has learned how to use quality engineering approaches (both the DOE and Taguchi methods) that can reduce variation and enhance a product’s quality. This book aims to demonstrate the power of these approaches, and shows how these methods can be implemented in either a manufacturing or nonmanufacturing organization. The payback in customer satisfaction and growth will be dramatic when these approaches are carefully conducted."

—Tzyy-Jang Tseng, Chairman, Unimicron Technology Corporation

"Professor Su has extensive experience contending with problems regarding quality in the manufacturing and service industries and has made eminent contributions to the field of quality engineering. I believe that he is one of the best-qualified persons to author a book on quality engineering."

—Noriaki Kano, Tokyo University of Science

Table of Contents



Robust Design

Quality Engineering

Structure of This Book


Fundamentals of Experimental Design

Basic Principle

Factorial Experiments

Two-Level Full Factorial Design

Two-Level Fractional Factorial Design

Three-Level Factorial Design

Steps of a DOE Project


Principles of Quality Engineering

Taguchi’s Perspectives

Noise Factors

Relationship between Quality Characteristics and Parameters

Classification of Parameters

Three Phases of Quality Engineering

Two-Step Optimization Procedure


Utilization of Orthogonal Arrays

Introduction of Orthogonal Arrays

The Use of Orthogonal Arrays


Linear Graphs

Orthogonal Arrays and Fractional Factorial Designs

Special Techniques for Modifying Orthogonal Arrays



Quality Loss Function and Static Signal-to-Noise Ratios

The Concept of Quality Loss

Taguchi’s Quality Loss

The Types of Quality Loss Functions

The Signal-to-Noise Ratio

Signal-to-Noise Ratios for Static Problems


Parameter Design for Static Characteristics

The Experiment Setup of Parameter Design

The Procedures of Static Parameter Design

Data Analysis of the Parameter Optimization Experiment

The Issue of Interactions

Examples of Parameter Design with Static Characteristics

Case Studies of Parameter Design with Static Characteristics

The Operating Window

Computer-Aided Parameter Design

Analysis of Discrete Data


Parameter Design for Dynamic Characteristics


Basic SN Ratios for Dynamic Problems

The Procedures of Dynamic Parameter Design

Examples of Parameter Design with Dynamic Characteristics

Case Studies of Parameter Design with Dynamic Characteristics

Other Types of Dynamic Problems


Implementing Parameter Design

Analysis in the Planning Stage

Selection of Quality Characteristic

Selection of Noise and Control Factors

Differences between Taguchi Methods and the Classical Experimental Design


Tolerance Design

The Concepts of Tolerance Design

The Procedures of Tolerance Design


Mahalanobis-Taguchi System

Mahalanobis Distance

Feature Selection

Mahalanobis-Taguchi System

Case Study: RF Inspection Process

Case Study: Pressure Ulcers Development


Response Surface Methodology

Introduction to Response Surface Methodology

Response Surfaces Designs

Fitting Models

Multi-Objective Optimization

Response Surface Approach for Process Robustness

Case Study: Improvement of the Fracture Resistance of Medium/Small-Sized TFT-LCD

Case Study: Optimization of the Performance of Inter-Metal Dielectric Process


Parameter Design Using Computational Intelligence


Neural Networks

Genetic Algorithms

Parameter Design Using Computational Intelligence

Case Studies






About the Author

Chao-Ton Su is a chair professor with the Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Hsinchu, Taiwan.

Subject Categories

BISAC Subject Codes/Headings:
TECHNOLOGY & ENGINEERING / Industrial Health & Safety