The book presents a systematic and efficient method to design high quality / reliability and high performance products / processes at low cost. Contains case studies from diverse engineering fields to describe Robust Design / Taguchi method.Some topics covered are: orthogonal arrays, Signal-to-Noise ratios as design quality metric, computer-aided robust design techniques, and more.
This book is written primarily for engineers and researchers who use statistical robust design for quality engineering and Six Sigma, and for statisticians who wish to know about the wide range of applications of experimental design in industry. It is a valuable guide and reference material for students, managers, quality improvement specialists and other professionals interested in Taguchi's robust design methods as well as the implementation of Six Sigma. This book can also be useful to those who would like to learn about the role of Robust Design within the Six Sigma (Improve phase) methodology and Design for Six Sigma (DFSS) (Optimize) methodology. It combines classical experimental design methods with those of Taguchi's robust designs, demonstrating their prowess in DFSS and suggesting new directions for the development of statistical design and analysis.
Robust Design is the procedure used by design engineers to reduce the effects of order to produce the highest quality products possible. This book includes real life case studies focusing on mechanical, chemical and imaging design that illustrate potential problems and their solutions and offers WinRobust Lite software and practice problems.
'Robust Design' brings together an eminent group of authors in a wide range of fields presenting aspects of robustness in biological, ecological and computational systems.
Based on deep theoretical as well as practical experience in Reliability and Quality Sciences, Robust Design Methodology for Reliability constructively addresses practical reliability problems. It offers a comprehensive design theory for reliability, utilizing robust design methodology and six sigma frameworks. In particular, the relation between un-reliability and variation and uncertainty is explored and reliability improvement measures in early product development stages are suggested. Many companies today utilise design for Six Sigma (DfSS) for strategic improvement of the design process, but often without explicitly describing the reliability perspective; this book explains how reliability design can relate to and work with DfSS and illustrates this with real–world problems. The contributors advocate designing for robustness, i.e. insensitivity to variation in the early stages of product design development. Methods for rational treatment of uncertainties in model assumptions are also presented. This book promotes a new approach to reliability thinking that addresses the design process and proneness to failure in the design phase via sensitivity to variation and uncertainty; includes contributions from both academics and industry practitioners with a broad scope of expertise, including quality science, mathematical statistics and reliability engineering; takes the innovative approach of promoting the study of variation and uncertainty as a basis for reliability work; includes case studies and illustrative examples that translate the theory into practice. Robust Design Methodology for Reliability provides a starting point for new thinking in practical reliability improvement work that will appeal to advanced designers and reliability specialists in academia and industry including fatigue engineers, product development and process/ quality professionals, especially those interested in and/ or using the DfSS framework.
Explains how to prevent quality problems in the early stages of product development and design, how to use the dynamic signal-to- noise ratio as the performance index for robustness of product functions, and how to evaluate methods of data collection. The book focuses on dynamic characteristics, foll.
Shows readers how to exploit the capabilities of the MATLAB® Robust Control and Control Systems Toolboxes to the fullest using practical robust control examples.
This book provides a unified collection of important, recent results for the design of robust controllers for uncertain systems. Most of the results presented are based on H? control theory, or its stochastic counterpart, risk sensitive control theory.Central to the philosophy of the book is the notion of an uncertain system. Uncertain systems are considered using several different uncertainty modeling schemes. These include norm bounded uncertainty, integral quadratic constraint (IQC) uncertainty and a number of stochastic uncertainty descriptions. In particular, the authors examine stochastic uncertain systems in which the uncertainty is outlined by a stochastic version of the IQC uncertainty description.For each class of uncertain systems covered in the book, corresponding robust control problems are defined and solutions discussed.
Lean production, has long been regarded as critical to business success in many industries. Over the last ten years, instruction in six sigma has been increasingly linked with learning about the elements of lean production. Introduction to Engineering Statistics and Lean Sigma builds on the success of its first edition (Introduction to Engineering Statistics and Six Sigma) to reflect the growing importance of the "lean sigma" hybrid. As well as providing detailed definitions and case studies of all six sigma methods, Introduction to Engineering Statistics and Lean Sigma forms one of few sources on the relationship between operations research techniques and lean sigma. Readers will be given the information necessary to determine which sigma methods to apply in which situation, and to predict why and when a particular method may not be effective. Methods covered include: • control charts and advanced control charts, • failure mode and effects analysis, • Taguchi methods, • gauge R&R, and • genetic algorithms. The second edition also greatly expands the discussion of Design For Six Sigma (DFSS), which is critical for many organizations that seek to deliver desirable products that work first time. It incorporates recently emerging formulations of DFSS from industry leaders and offers more introductory material on the design of experiments, and on two level and full factorial experiments, to help improve student intuition-building and retention. The emphasis on lean production, combined with recent methods relating to Design for Six Sigma (DFSS), makes Introduction to Engineering Statistics and Lean Sigma a practical, up-to-date resource for advanced students, educators, and practitioners.
The Robust Federation offers a comprehensive approach to the study of federalism. Jenna Bednar demonstrates how complementary institutions maintain and adjust the distribution of authority between national and state governments. These authority boundaries matter - for defense, economic growth, and adequate political representation - and must be defended from opportunistic transgression. From Montesquieu to Madison, the legacy of early institutional analysis focuses attention on the value of competition between institutions, such as the policy moderation produced through separated powers. Bednar offers a reciprocal theory: in an effective constitutional system, institutions complement one another; each makes the others more powerful. Diverse but complementary safeguards - including the courts, political parties, and the people - cover different transgressions, punish to different extents, and fail under different circumstances. The analysis moves beyond equilibrium conceptions and explains how the rules that allocate authority are not fixed but shift gradually. Bednar's rich theoretical characterization of complementary institutions provides the first holistic account of federal robustness.
