DESIGN OF EXPERIMENTS FOR ENGINEERS AND SCIENTISTS
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By Jiju Antony, Professor of Quality Management and Deputy Director of the Institute of Operations management with the University of Strathclyde
Description The 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.
Audience
Manufacturing engineers, project engineers, quality engineers, quality managers, production engineers and students.
Contents Preface.
Acknowledgements.
INTRODUCTION TO INDUSTRIAL EXPERIMENTATION
Introduction.
Some fundamental
and practical issues in industrial experimentation.
Summary.
Exercises.
References.
FUNDAMENTALS OF
DESIGN OF EXPERIMENTS:
Introduction.
Basic principles of Design of Experiments:
Randomization.
Replication.
Blocking.
Degrees of freedom.
Confounding.
Design resolution.
Metrology
considerations for industrial designed experiments:
Measurement system capability.
Some tips for the development of a measurement
system.
Selection of quality characteristics for industrial experiments.
Exercises.
References.
UNDERSTANDING
KEY INTERACTIONS IN PROCESSES:
Introduction.
Alternative method for calculating the two order interaction
effect.
Synergistic interaction vs antagonistic interaction.
Scenario 1.
Scenario
2.
Summary.
Exercises.
References.
A SYSTEMATIC METHODOLOGY FOR DESIGN OF EXPERIMENTS:
Introduction.
Barriers in the successful application of DOE.
A practical methodology for DOE:
Planning phase.
Designing
phase.
Conducting phase.
Analysing phase.
Analytical tools of DOE:
Main effects plot.
Interactions plots.
Cube plots.
Pareto plot of factor effects.
Normal Probability Plot of factor effects.
Normal Probability Plot of residuals.
Response surface plots
and regression models.
Model building for predicting response function.
Confidence interval for the mean response.
Summary.
Exercises.
References.
SCREENING DESIGNS:
Introduction.
Geometric
and non-geometric P-B designs.
Summary.
Exercises.
References.
FULL FACTORIAL DESIGNS:
Introduction.
Example of a 2 squared full factorial design:
Objective 1: Determination of main/interaction effects which influence
mean plating thickness.
Objective 2: Determination of main/interaction effects which influence variability in plating thickness.
Objective
4: How to achieve a target plating thickness of 120 units?
Example of a 2 to the power of 3 full factorial design:
Objective
1: To identify the significant main/interaction effects which affect the process yield.
Objective 2: To identify the significant main/interaction
effects which affect the variability in process yield.
Objective 3: What is the optimal process condition?
Example of a 2 to
the power of 4 full factorial design:
Objective 1: Which of the main/interaction effects affect mean crack length?
Objective
2: Which of the main/interaction effects affect variability in crack length?
Objective 3: What is the optimal process condition to minimize
mean crack length?
Summary.
Exercises.
References.
FRACTIONAL FACTORIAL DESIGNS:
Introduction.
Construction of half-fractional factorial designs.
Example of a 2 to the power of (7-4) factorial design.
An application of 2-level fractional factorial design.
Example of a 2 to the power of (5-1) factorial design:
Objective 1: To identify the factors which influence the mean free height.
Objective 2: To identify the factors which affect variability
in the free height of leaf springs.
How do we select the optimal factor settings to minimize variability in free height?
Summary.
Exercises.
References.
SOME USEFUL AND PRACTICAL TIPS FOR MAKING YOUR EXPERIMENTS SUCCESSFUL:
Introduction:
Get a clear understanding of a problem.
Project selection.
Conduct exhaustive and detailed brainstorming session.
Teamwork and selection
of a team foe experimentation.
Select the continuous measurable quality characteristics or responses for the experiment.
Choice of an
appropriate Experimental Design.
Iterative experimentation.
Randomize the experimental trial order.
Replicate to dampen the effect of
noise or uncontrolled variation.
Improve the efficiency of experimentation using blocking strategy.
Understanding the confounding pattern
of factor effects.
Perform confirmatory runs/experiments.
Summary.
Exercises.
References.
CASE STUDIES:
Introduction.
Case studies:
Optimization of a radiographic quality welding of cast iron.
Reducing process
variability using Experimental Design technique objective of the experiment.
Slashing scrap rate using fractional experiments.
Optimizing
the time of flight of a paper helicopter.
Optimizing a wire bonding process using Design of Experiments.
Training for Design of Experiments
using a catapult.
Optimization of core tube life using designed experiments.
Optimization of a spot welding process using Design of Experiments.
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