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This textbook provides the tools, techniques, and industry examples needed for the successful implementation of design of experiments (DoE) in engineering and manufacturing applications. It contains a high-level engineering analysis of key issues in the design, development, and successful analysis of industrial DoE, focusing on the design aspect of the experiment and then on interpreting the results. Statistical analysis is shown without formula derivation, and readers are directed as to the meaning of each term in the statistical analysis. Industrial Design of Experiments: A Case Study Approach for Design and Process Optimization is designed for graduate-level DoE, engineering design, and general statistical courses, as well as professional education and certification classes. Practicing engineers and managers working in multidisciplinary product development will find it to be an invaluable reference that provides all the information needed to accomplish a successful DoE.
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
"This is an engaging and informative book on the modern practice of experimental design. The authors' writing style is entertaining, the consulting dialogs are extremely enjoyable, and the technical material is presented brilliantly but not overwhelmingly. The book is a joy to read. Everyone who practices or teaches DOE should read this book." - Douglas C. Montgomery, Regents Professor, Department of Industrial Engineering, Arizona State University "It's been said: 'Design for the experiment, don't experiment for the design.' This book ably demonstrates this notion by showing how tailor-made, optimal designs can be effectively employed to meet a client's actual needs. It should be required reading for anyone interested in using the design of experiments in industrial settings." —Christopher J. Nachtsheim, Frank A Donaldson Chair in Operations Management, Carlson School of Management, University of Minnesota This book demonstrates the utility of the computer-aided optimal design approach using real industrial examples. These examples address questions such as the following: How can I do screening inexpensively if I have dozens of factors to investigate? What can I do if I have day-to-day variability and I can only perform 3 runs a day? How can I do RSM cost effectively if I have categorical factors? How can I design and analyze experiments when there is a factor that can only be changed a few times over the study? How can I include both ingredients in a mixture and processing factors in the same study? How can I design an experiment if there are many factor combinations that are impossible to run? How can I make sure that a time trend due to warming up of equipment does not affect the conclusions from a study? How can I take into account batch information in when designing experiments involving multiple batches? How can I add runs to a botched experiment to resolve ambiguities? While answering these questions the book also shows how to evaluate and compare designs. This allows researchers to make sensible trade-offs between the cost of experimentation and the amount of information they obtain.
This book covers design of experiments (DoE) applied in production engineering as a combination of manufacturing technology with applied management science. It presents recent research advances and applications of design experiments in production engineering and the chapters cover metal cutting tools, soft computing for modelling and optmization of machining, waterjet machining of high performance ceramics, among others.
Statistics is a key characteristic that assists a wide variety of professions including business, government, and factual sciences. Companies need data calculation to make informed decisions that help maintain their relevance. Design of experiments (DOE) is a set of active techniques that provides a more efficient approach for industries to test their processes and form effective conclusions. Experimental design can be implemented into multiple professions, and it is a necessity to promote applicable research on this up-and-coming method. Design of Experiments for Chemical, Pharmaceutical, Food, and Industrial Applications is a pivotal reference source that seeks to increase the use of design of experiments to optimize and improve analytical methods and productive processes in order to use less resources and time. While highlighting topics such as multivariate methods, factorial experiments, and pharmaceutical research, this publication is ideally designed for industrial designers, research scientists, chemical engineers, managers, academicians, and students seeking current research on advanced and multivariate statistics.
This textbook provides the tools, techniques, and industry examples needed for the successful implementation of design of experiments (DoE) in engineering and manufacturing applications. It contains a high-level engineering analysis of key issues in the design, development, and successful analysis of industrial DoE, focusing on the design aspect of the experiment and then on interpreting the results. Statistical analysis is shown without formula derivation, and readers are directed as to the meaning of each term in the statistical analysis. Industrial Design of Experiments: A Case Study Approach for Design and Process Optimization is designed for graduate-level DoE, engineering design, and general statistical courses, as well as professional education and certification classes. Practicing engineers and managers working in multidisciplinary product development will find it to be an invaluable reference that provides all the information needed to accomplish a successful DoE. Presents classical versus Taguchi DoE methodologies as well as techniques developed by the author for successful DoE; Offers a step-wise approach to DoE optimization and interpretation of results; Includes industrial case studies, worked examples and detailed solutions to problems.
Fulfill the practical potential of DOE-with a powerful, 16-step approach for applying the Taguchi method Over the past decade, Design of Experiments (DOE) has undergone great advances through the work of the Japanese management guru Genechi Taguchi. Yet, until now, books on the Taguchi method have been steeped in theory and complicated statistical analysis. Now this trailblazing work translates the Taguchi method into an easy-to-implement 16-step system. Based on Ranjit Roy's successful Taguchi training course, this extensively illustrated book/CD-ROM package gives readers the knowledge and skills necessary to understand and apply the Taguchi method to engineering projects-from theory and applications to hands-on analysis of the data. It is suitable for managers and technicians without a college-level engineering or statistical background, and its self-study pace-with exercises included in each chapter-helps readers start using Taguchi DOE tools on the job quickly. Special features include: * An accompanying CD-ROM of Qualitek-4 software, which performs calculations and features all example experiments described in the book * Problem-solving exercises relevant to actual engineering situations, with solutions included at the end of the text * Coverage of two-, three-, and four-level factors, analysis of variance, robust designs, combination designs, and more Engineers and technical personnel working in process and product design-as well as other professionals interested in the Taguchi method-will find this book/CD-ROM a tremendously important and useful asset for making the most of DOE in their work.
Design of Experiments: A Modern Approach introduces readers to planning and conducting experiments, analyzing the resulting data, and obtaining valid and objective conclusions. This innovative textbook uses design optimization as its design construction approach, focusing on practical experiments in engineering, science, and business rather than orthogonal designs and extensive analysis. Requiring only first-course knowledge of statistics and familiarity with matrix algebra, student-friendly chapters cover the design process for a range of various types of experiments. The text follows a traditional outline for a design of experiments course, beginning with an introduction to the topic, historical notes, a review of fundamental statistics concepts, and a systematic process for designing and conducting experiments. Subsequent chapters cover simple comparative experiments, variance analysis, two-factor factorial experiments, randomized complete block design, response surface methodology, designs for nonlinear models, and more. Readers gain a solid understanding of the role of experimentation in technology commercialization and product realization activities—including new product design, manufacturing process development, and process improvement—as well as many applications of designed experiments in other areas such as marketing, service operations, e-commerce, and general business operations.