Antony J. Design of Experiments for Engineers and Scientists

2nd Ed. — Elsevier, 2014. — 155 p. — ISBN: 9780080994178.Key Features
Written in non-statistical language, the book is an essential and accessible text for scientists and engineers who want to learn how to use DoE
Explains why teaching DoE techniques in the improvement phase of Six Sigma is an important part of problem solving methodology
New edition includes a full chapter on DoE for services as well as case studies illustrating its wider application in the service industryDescriptionThe tools and techniques used in Design of Experiments (DoE) have been proven successful in meeting the challenge of continuous improvement in many manufacturing organisations over the last two decades. However research has shown that application of this powerful technique in many companies is limited due to a lack of statistical knowledge required for its effective implementation.Although many books have been written on 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 through using statistical methods and readers will find the concepts in this book both familiar and easy to understand.This new edition includes a chapter on the role of DoE within Six Sigma methodology and also shows through the use of simple case studies its importance in the service industry. It is essential reading for 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.
Readership
Manufacturing engineers, project engineers, quality engineers, quality managers, production engineers and students.Introduction to Industrial ExperimentationSome fundamental and practical issues in industrial experimentation.Exercises.Fundamentals of Design of Experiments: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.Understanding Key Interactions in Processes:Alternative method for calculating the two order interaction effect.
Synergistic interaction vs antagonistic interaction.
Scenario 1.
Scenario 2.Exercises.A Systematic Methodology for Design of Experiments: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.Exercises.Screening Designs:Geometric and non-geometric P-B designs.Exercises.Full Factorial Designs: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?Exercises.Fractional Factorial Designs: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?Exercises.Some Useful and Practical Tips for Making Your Experiments Successful: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.Exercises.Case Studies: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.Design of Experiments and Its Applications in Service IndustryFundamental differences in the Manufacturing and Service Organizations
Design of Experiments in Service Industry: Fundamental Challenges
Benefits of Design of Experiments in Service/Mon-manufacturing Industry
Design of Experiments(DoE): Case Examples from the Service Industry
Role of computer simulation models within DoEExercisesDesign of Experiments and Its Role Within Six Sigma
What is Six Sigma?
How Six Sigma is different from other Quality Improvement initiatives of the past
Who makes Six Sigma Work?
Six Sigma Methodology (DMAIC methodology)
DoE and its role within Six SigmaExercises