## Design of Experiments for Engineers and ScientistsThe tools and technique used in the Design of Experiments (DOE) have been proved successful in meeting the challenge of continuous improvement over the last 15 years. However, research has shown that applications of these techniques in small and medium-sized manufacturing companies are limited due to a lack of statistical knowledge required for their effective implementation. Although many books have been written in this subject, they are mainly by statisticians, for statisticians and not appropriate for engineers. Design of Experiments for Engineers and Scientists overcomes the problem of statistics by taking a unique approach using graphical tools. The same outcomes and conclusions are reached as by those using statistical methods and readers will find the concepts in this book both familiar and easy to understand. The book treats Planning, Communication, Engineering, Teamwork and Statistical Skills in separate chapters and then combines these skills through the use of many industrial case studies. Design of Experiments forms part of the suite of tools used in Six Sigma.Key features: * Provides essential DOE techniques for process improvement initiatives * Introduces simple graphical techniques as an alternative to advanced statistical methods – reducing time taken to design and develop prototypes, reducing time to reach the market * Case studies place DOE techniques in the context of different industry sectors * An excellent resource for the Six Sigma training program This book will be useful to engineers and scientists from all disciplines tackling all kinds of manufacturing, product and process quality problems and will be an ideal resource for students of this topic. Dr Jiju Anthony is Senior Teaching Fellow at the International Manufacturing Unit at Warwick University. He is also a trainer and consultant in DOE and has worked as such for a number of companies including Motorola, Vickers, Procter and Gamble, Nokia, Bosch and a large number of SMEs.* Provides essential DOE techniques for process improvement initiatives * Introduces simple graphical techniques as an alternative to advanced statistical methods - reducing time taken to design and conduct tests * Case studies place DOE techniques in the context of different industry sectors |

### What people are saying - Write a review

User Review - Flag as inappropriate

I struggled with Chapter 7 (Fractional factorial designs) and sought out other sources to understand "design generator" and "defining relation." I suggest "Statistical Quality Design and Control" by DeVor et al for a step-by-step explanation of fractional design for beginners. DOE using fractional design should have been the most in-depth chapter, but fell short.

The other chapters were helpful and easy to read.

User Review - Flag as inappropriate

required

### Contents

1 | |

6 | |

Understanding key interactions in processes | 17 |

A systematic methodology for Design of Experiments | 29 |

Screening designs | 44 |

Full factorial designs | 54 |

### Other editions - View all

### Common terms and phrases

aliased core tube cube plot degrees of freedom design matrix Design of Experiments design parameters error example experimental design experimental layout experimental trials factors at 2-levels fractional factorial design full factorial design full factorial experiment illustrates impact important improvement industrial designed experiment industrial experiments interaction effects interaction plot Labels Low level level High level ln(SD log(SD lurking variables main and interaction Main effects plot mean crack length measurement system minimize Minitab software moulding process Normal probability plot normally distributed number of experimental number of factors objective optimal condition optimal settings P–B design Pareto plot plating solution temperature plot of effects pressure problem process or design process parameters process variables pull strength quality characteristics Ramp random regression model response of interest response values runs shown in Table strong interaction Taguchi trial condition two-factor interactions variation wave-soldering weld strength welding current wire bonding