Optimized operating conditions for complex systems can be attained by using advanced combinations of numerical and statistical methodologies. One of the most efficient and straightforward solutions relies on the application of statistical methods with an emphasis on the design of experiments (DoEs). Throughout the book, the design and analysis of experiments are conducted involving several approaches, namely, Taguchi, response surface methods, statistical correlations, or even fractional factorial and model-based evolutionary operation designs. This book not only presents a theoretical overview about the different approaches but also contains material that covers the use of the experimental analysis applied to several chemical processes. Some chapters highlight the use of software products to assist experimenters in both the design and analysis stages. It helps graduate students, teachers, researchers, and other professionals who are interested in chemical process optimization and also provides a good basis of theoretical knowledge and valuable insights into the technical details of these tools as well as explains common pitfalls to avoid. The world's leading pharmaceutical companies and local governments are trying to achieve their eradication.

While existing books related to DOE are focused either on process or mixture factors or analyze specific tools from DOE science, this text is structured both horizontally and vertically, covering the three most common objectives of any experimental research: * screening designs * mathematical modeling, and * optimization. Written in a simple and lively manner and backed by current chemical product studies from all around the world, the book elucidates basic concepts of statistical methods, experiment design and optimization techniques as applied to chemistry and chemical engineering. Throughout, the focus is on unifying the theory and methodology of optimization with well-known statistical and experimental methods. The author draws on his own experience in research and development, resulting in a work that will assist students, scientists and engineers in using the concepts covered here in seeking optimum conditions for a chemical system or process. With 441 tables, 250 diagrams, as well as 200 examples drawn from current chemical product studies, this is an invaluable and convenient source of information for all those involved in process optimization.

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 book forms an excellent basis for the development of intelligent manufacturing system for Industry 4.0, digital and distributed manufacturing, and factories for future. This book of new developments and advancement in intelligent control and optimization system for production engineering serves as a good companion to scholars, manufacturing companies, and RTO to improve the efficiency of production systems.

In a world where innovation and sustainability are paramount, Fundamentals of Design of Experiments for Automotive Engineering: Volume I serves as a definitive guide to harnessing the power of statistical thinking in product development. As first of four volumes in SAE International’s DOE for Product Reliability Growth series, this book presents a practical, application-focused approach by emphasizing DOE as a dynamic tool for automotive engineers. It showcases real-world examples, demonstrating how process improvements and system optimizations can significantly enhance product reliability. The author, Yung Chiang, leverages extensive product development expertise to present a comprehensive process that ensures product performance and reliability throughout its entire lifecycle. Whether individuals are involved in research, design, testing, manufacturing, or marketing, this essential reference equips them with the skills needed to excel in their respective roles. This book explores the potential of Reliability and Sustainability with DOE, featuring the following topics: - Fundamental prerequisites for deploying DOE: Product reliability processes, measurement uncertainty, failure analysis, and design for reliability. - Full factorial design 2K: A system identification tool for relating objectives to factors and understanding main and interactive effects. - Fractional factorial design 2RK-P: Ideal for identifying main effects and 2-factor interactions. - General fractional factorial design LK-P: Systematically identification of significant inputs and analysis of nonlinear behaviors. - Composite designs as response surface methods: Resolving interactions and optimizing decisions with limited factors. - Adapting to practical challenges with “short” DOE: Leveraging optimization schemes like D-optimality, and A-optimality for optimal results. Readers are encouraged not to allow product failures to hinder progress but to embrace the "statistical thinking" embedded in DOE. This book can illuminate the path to designing products that stand the test of time, resulting in satisfied customers and thriving businesses. (ISBN 9781468606027, ISBN 9781468606034, ISBN 9781468606041, DOI 10.4271/9781468606034)

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

Failure modes and effects analysis (FMEA); Reliability; Product Development; Design Process; Test Procedures "Explore Product Design and Testing for Automotive Engineering: Volume II, an essential guide reshaping vehicle manufacturing with unprecedented reliability. As part of SAE International’s DOE for Product Reliability Growth series, this practical resource introduces cutting-edge methodologies crucial for predicting and improving product reliability in an era of automotive electrification. The book navigates statistical tolerance design, showcasing how variability in part fabrication and assembly can enhance reliability and sustainability. Key topics include: - Statistical tolerance design's impact on manufacturing and material selection, focusing on non-normal distributions' effects on product assembly and cost. Methods like maximum likelihood estimators and Monte Carlo simulations are used for assembly strategy synthesis. - Reliability DOEs using log-location-scale distributions to estimate lifetimes of non-normally distributed components, especially in accelerated life testing. It covers transformations optimizing parts and system designs under the lognormal distribution. - Weibull distribution (DOE-W) for characterizing lifetimes affected by various failure modes, detailing parameter assessment methods and real-world applications. The book also introduces reliability design of experiments based on the exponential distribution (DOE-E). - Importance of predicting lifecycles and enhancing reliability through qualitative and stepwise accelerated life tests. Integration of physics of failure with statistical methods like Weibull statistics and lognormal approximation enhances analysis credibility. - Inferential mechanisms such as the Arrhenius and Eyring models in predicting automotive component lifecycles, refining product life prediction based on reliability DOEs. Whether you're an engineer, researcher, or automotive professional, this book equips you to navigate reliability engineering confidently. Revolutionize your approach to product design and testing with Product Design and Testing for Automotive Engineering, your definitive companion in shaping the future of automotive reliability." (ISBN 9781468607703 ISBN 9781468607697 ISBN 9781468607727 DOI 10.4271/9781468607697)