Richard D Braatz – författare

Artificial Intelligence in Manufacturing: Concepts and Methods explains the most successful emerging techniques for applying AI to engineering problems. Artificial intelligence is increasingly being applied to all engineering disciplines, producing more insights into how we understand the world and allowing us to create products in new ways. This book unlocks the advantages of this technology for manufacturing by drawing on work by leading researchers who have successfully developed methods that can apply to a range of engineering applications.

The book addresses educational challenges needed for widespread implementation of AI and also provides detailed technical instructions for the implementation of AI methods. Drawing on research in computer science, physics and a range of engineering disciplines, this book tackles the interdisciplinary challenges of the subject to introduce new thinking to important manufacturing problems.

Presents AI concepts from the computer science field using language and examples designed to inspire engineering graduates Provides worked examples throughout to help readers fully engage with the methods described Includes concepts that are supported by definitions for key terms and chapter summaries

Artificial Intelligence in Manufacturing: Applications and Case Studies provides detailed technical descriptions of emerging applications of AI in manufacturing using case studies to explain implementation. Artificial intelligence is increasingly being applied to all engineering disciplines, producing insights into how we understand the world and allowing us to create products in new ways. This book unlocks the advantages of this technology for manufacturing by drawing on work by leading researchers who have successfully used it in a range of applications. Processes including additive manufacturing, pharmaceutical manufacturing, painting, chemical engineering and machinery maintenance are all addressed.

Case studies, worked examples, basic introductory material and step-by-step instructions on methods make the work accessible to a large group of interested professionals.

Explains innovative computational tools and methods in a practical and systematic way Addresses a wide range of manufacturing types, including additive, chemical and pharmaceutical Includes case studies from industry that describe how to overcome the challenges of implementing these methods in practice

Artificial Intelligence in Manufacturing: Applications and Case Studies provides detailed technical descriptions of emerging applications of AI in manufacturing using case studies to explain implementation. Artificial intelligence is increasingly being applied to all engineering disciplines, producing insights into how we understand the world and allowing us to create products in new ways. This book unlocks the advantages of this technology for manufacturing by drawing on work by leading researchers who have successfully used it in a range of applications. Processes including additive manufacturing, pharmaceutical manufacturing, painting, chemical engineering and machinery maintenance are all addressed.Case studies, worked examples, basic introductory material and step-by-step instructions on methods make the work accessible to a large group of interested professionals.- Explains innovative computational tools and methods in a practical and systematic way- Addresses a wide range of manufacturing types, including additive, chemical and pharmaceutical- Includes case studies from industry that describe how to overcome the challenges of implementing these methods in practice

Artificial Intelligence in Manufacturing: Concepts and Methods explains the most successful emerging techniques for applying AI to engineering problems. Artificial intelligence is increasingly being applied to all engineering disciplines, producing more insights into how we understand the world and allowing us to create products in new ways. This book unlocks the advantages of this technology for manufacturing by drawing on work by leading researchers who have successfully developed methods that can apply to a range of engineering applications.The book addresses educational challenges needed for widespread implementation of AI and also provides detailed technical instructions for the implementation of AI methods. Drawing on research in computer science, physics and a range of engineering disciplines, this book tackles the interdisciplinary challenges of the subject to introduce new thinking to important manufacturing problems.- Presents AI concepts from the computer science field using language and examples designed to inspire engineering graduates- Provides worked examples throughout to help readers fully engage with the methods described- Includes concepts that are supported by definitions for key terms and chapter summaries

Digital Twins in Manufacturing: Applications and Case Studies explores digital twin technologies, demonstrating how digital twins enhance manufacturing processes, optimize efficiency, and facilitate informed decision-making. By bridging the gap between theoretical knowledge and practical implementation, the book equips professionals with the tools needed to leverage digital twins for improved product quality and operational effectiveness. It covers a wide array of topics, including digital twins for biomanufacturing, polymerization processes, additive manufacturing, and energy industries. Readers will find detailed discussions on predictive maintenance, process optimization, and the integration of AI and IoT strategies.Each chapter presents actionable insights and step-by-step implementation guides, addressing real-world challenges and showcasing successful applications in smart factories and high-risk environments. By bridging the gap between theoretical knowledge and practical implementation, the book equips professionals with the tools needed to leverage digital twins for improved product quality and operational effectiveness.Includes real-world case studiesPresents a step-by-step implementation guideOutlines predictive maintenance and process optimization techniquesDiscusses AI and IoT integration strategiesForecasts future trends and innovations in digital twin technology

Digital Twins in Manufacturing: Concepts and Methods explores digital twin technologies, providing innovative perspectives and practical insights that transcend traditional coverage. The book addresses the complexities of understanding and applying digital twin concepts in manufacturing environments where professionals often struggle to bridge the gap between physical and virtual systems. Sections cover critical areas such as the systems components of digital twins, foundational definitions, modeling and simulation techniques, data integration, and the role of AI and machine learning. Readers will also gain insights into communication technologies, interoperability, security considerations, and the development lifecycle of digital twins.Each chapter is designed to equip professionals with the knowledge needed to effectively replicate and monitor real-world assets, ultimately enhancing operational efficiency and driving innovation. This is an invaluable asset for engineers, data scientists, IT professionals, and researchers in manufacturing and industrial engineering.Explains fundamental concepts of digital twin modelingDemonstrates advanced simulation and data integration techniquesOutlines lifecycle management methods that guide readers through the stages of digital twin implementationEvaluates predictive analytics and maintenance modelsExplores scalability and integration strategies

Early and accurate fault detection and diagnosis for modern chemical plants can minimise downtime, increase the safety of plant operations, and reduce manufacturing costs. The process-monitoring techniques that have been most effective in practice are based on models constructed almost entirely from process data. The goal of the book is to present the theoretical background and practical techniques for data-driven process monitoring. Process-monitoring techniques presented include: Principal component analysis; Fisher discriminant analysis; Partial least squares; Canonical variate analysis.The text demonstrates the application of all of the data-driven process monitoring techniques to the Tennessee Eastman plant simulator - demonstrating the strengths and weaknesses of each approach in detail. This aids the reader in selecting the right method for his process application. Plant simulator and homework problems in which students apply the process-monitoring techniques to a nontrivial simulated process, and can compare their performance with that obtained in the case studies in the text are included. A number of additional homework problems encourage the reader to implement and obtain a deeper understanding of the techniques.The reader will obtain a background in data-driven techniques for fault detection and diagnosis, including the ability to implement the techniques and to know how to select the right technique for a particular application.

Sheet and film processes include coating, papermaking, metal rolling, and polymer film extrusion. Products produced by these processes include paper, bumper stickers, plastic bags, windshield safety glass, and sheet metal. The total capitalization of industries that rely on these processes is well over $ 500 billion worldwide. These processes are notorious for being difficult to control.The goal of this book is to present the theoretical background and practical techniques for the identification and control of sheet and film processes. It is explained why many existing industrial control systems perform poorly for sheet and film processes. Identification and control algorithms are described and illustrated which provide consistent and reliable product quality. These algorithms include an experimental design technique that ensures that informative data are collected during input-output experimentation, model identification techniques that produce a process model and an estimate of its accuracy, and control techniques that take into account actuator constraints as well as robustness to model uncertainties.The algorithms covered in this book are truly the state of the art. Variations on some of the algorithms have been implemented on industrial sheet and film processes. Other algorithms are in various stages of implementation. All of the algorithms have been applied to realistic simulation models constructed from industrial plant data; many of these studies are included in this book.

Early and accurate fault detection and diagnosis for modern chemical plants can minimise downtime, increase the safety of plant operations, and reduce manufacturing costs. The process-monitoring techniques that have been most effective in practice are based on models constructed almost entirely from process data. The goal of the book is to present the theoretical background and practical techniques for data-driven process monitoring. Process-monitoring techniques presented include: Principal component analysis; Fisher discriminant analysis; Partial least squares; Canonical variate analysis.The text demonstrates the application of all of the data-driven process monitoring techniques to the Tennessee Eastman plant simulator - demonstrating the strengths and weaknesses of each approach in detail. This aids the reader in selecting the right method for his process application. Plant simulator and homework problems in which students apply the process-monitoring techniques to a nontrivial simulated process, and can compare their performance with that obtained in the case studies in the text are included. A number of additional homework problems encourage the reader to implement and obtain a deeper understanding of the techniques.The reader will obtain a background in data-driven techniques for fault detection and diagnosis, including the ability to implement the techniques and to know how to select the right technique for a particular application.

Sheet and film processes include coating, papermaking, metal rolling, and polymer film extrusion. Products produced by these processes include paper, bumper stickers, plastic bags, windshield safety glass, and sheet metal. The total capitalization of industries that rely on these processes is well over $ 500 billion worldwide. These processes are notorious for being difficult to control.The goal of this book is to present the theoretical background and practical techniques for the identification and control of sheet and film processes. It is explained why many existing industrial control systems perform poorly for sheet and film processes. Identification and control algorithms are described and illustrated which provide consistent and reliable product quality. These algorithms include an experimental design technique that ensures that informative data are collected during input-output experimentation, model identification techniques that produce a process model and an estimate of its accuracy, and control techniques that take into account actuator constraints as well as robustness to model uncertainties.The algorithms covered in this book are truly the state of the art. Variations on some of the algorithms have been implemented on industrial sheet and film processes. Other algorithms are in various stages of implementation. All of the algorithms have been applied to realistic simulation models constructed from industrial plant data; many of these studies are included in this book.

Early and accurate fault detection and diagnosis for modern chemical plants can minimise downtime, increase the safety of plant operations, and reduce manufacturing costs. The process-monitoring techniques that have been most effective in practice are based on models constructed almost entirely from process data. The goal of the book is to present the theoretical background and practical techniques for data-driven process monitoring. Process-monitoring techniques presented include: Principal component analysis; Fisher discriminant analysis; Partial least squares; Canonical variate analysis.The text demonstrates the application of all of the data-driven process monitoring techniques to the Tennessee Eastman plant simulator - demonstrating the strengths and weaknesses of each approach in detail. This aids the reader in selecting the right method for his process application. Plant simulator and homework problems in which students apply the process-monitoring techniques to a nontrivial simulated process, and can compare their performance with that obtained in the case studies in the text are included. A number of additional homework problems encourage the reader to implement and obtain a deeper understanding of the techniques.The reader will obtain a background in data-driven techniques for fault detection and diagnosis, including the ability to implement the techniques and to know how to select the right technique for a particular application.