Graphical Methods For The Design Of Experiments
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Graphical Methods for the Design of Experiments
Author: Russell R. Barton
language: en
Publisher: Springer Science & Business Media
Release Date: 2012-12-06
Graphical methods have played an important role in the statistical analysis of experimental data, but have not been used as extensively for experiment design, at least as it is presented in most design of experiments texts. Yet graphical methods are particularly attractive for the design of experiments because they exploit our creative right-brain capabilities. Creative activity is clearly important in any kind of design, certainly for the design ofan experiment. The experimenter must somehow select a set of run conditions that will meet the goals for a particular experiment in a cost-efficient way. Graphical Methods for Experiment Design goes beyond graphical methods for choosing run conditions for an experiment. It looks at the entire pre-experiment planning process, and presents in one place a collection of graphical methods for defining experiment goals, identifying and classifying variables, for choosing a model, for developing a design, and for assessing the adequacy of a design for estimating the unknown coefficients in the proposed statistical model. In addition, tools for developing a design also provide a platform for viewing the results of the experiment, a platform that provides insights that cannot be obtained by examination ofregression coefficients. These techniques can be applied to a variety of situations, including experimental runs of computer simulation models. Factorial and fractional-factorial designs are the focus of the graphical representations, although mixture experiments and experiments involving random effects and blocking are designed and represented in similar ways.
APPLIED DESIGN OF EXPERIMENTS AND TAGUCHI METHODS
Author: K. KRISHNAIAH
language: en
Publisher: PHI Learning Pvt. Ltd.
Release Date: 2012-01-18
Design of experiments (DOE) is an off-line quality assurance technique used to achieve best performance of products and processes. This book covers the basic ideas, terminology, and the application of techniques necessary to conduct a study using DOE. The text is divided into two parts—Part I (Design of Experiments) and Part II (Taguchi Methods). Part I (Chapters 1–8) begins with a discussion on basics of statistics and fundamentals of experimental designs, and then, it moves on to describe randomized design, Latin square design, Graeco-Latin square design. In addition, it also deals with statistical model for a two-factor and three-factor experiments and analyses 2k factorial, 2k-m fractional factorial design and methodology of surface design. Part II (Chapters 9–16) discusses Taguchi quality loss function, orthogonal design, objective functions in robust design. Besides, the book explains the application of orthogonal arrays, data analysis using response graph method/analysis of variance, methods for multi-level factor designs, factor analysis and genetic algorithm. This book is intended as a text for the undergraduate students of Industrial Engineering and postgraduate students of Mechtronics Engineering, Mechanical Engineering, and Statistics. In addition, the book would also be extremely useful for both academicians and practitioners KEY FEATURES : Includes six case studies of DOE in the context of different industry sector. Provides essential DOE techniques for process improvement. Introduces simple graphical methods for reducing time taken to design and develop products.
Design of Experiments for Engineers and Scientists
The 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. - 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 industry