Intelligent Manufacturing and Industrial Engineering – serie

This book looks at industry change patterns and innovations (such as artificial intelligence, machine learning, big data analysis, and blockchain support and efficiency technology) that are speeding up industrial transformation, industrial infrastructure, biodiversity, and productivity.This book focuses on real-world industrial applications and case studies to provide for a wider knowledge of intelligent manufacturing. It also offers insights into manufacturing, logistics, and supply chain, where systems have undergone an industrial transformation. It discusses current research of machine learning along with blockchain techniques that can fill the gap between research and industrial exposure. It goes on to cover the effects that the Fourth Industrial Revolution has on industrial infrastructures and looks at the current industry change patterns and innovations that are accelerating industrial transformation activities.Researchers, scholars, and students from different countries will appreciate this book for its real-world applications and knowledge acquisition. This book targets manufacturers, industry owners, product developers, scientists, logistics, and supply chain engineers.Focuses on real-world industrial applications and case studies to provide for a wider knowledge of intelligent manufacturingOffers insights into manufacturing, logistics, and supply chain where systems have undergone an industrial transformationDiscusses current research of machine learning along with blockchain techniques that can fill the gap between research and industrial exposureCovers the effects that the 4th Industrial Revolution has on industrial infrastructuresLooks at industry change patterns and innovations that are speeding up industrial transformation activities

This book looks at industry change patterns and innovations (such as artificial intelligence, machine learning, big data analysis, and blockchain support and efficiency technology) that are speeding up industrial transformation, industrial infrastructure, biodiversity, and productivity.This book focuses on real-world industrial applications and case studies to provide for a wider knowledge of intelligent manufacturing. It also offers insights into manufacturing, logistics, and supply chain, where systems have undergone an industrial transformation. It discusses current research of machine learning along with blockchain techniques that can fill the gap between research and industrial exposure. It goes on to cover the effects that the Fourth Industrial Revolution has on industrial infrastructures and looks at the current industry change patterns and innovations that are accelerating industrial transformation activities.Researchers, scholars, and students from different countries will appreciate this book for its real-world applications and knowledge acquisition. This book targets manufacturers, industry owners, product developers, scientists, logistics, and supply chain engineers.Focuses on real-world industrial applications and case studies to provide for a wider knowledge of intelligent manufacturingOffers insights into manufacturing, logistics, and supply chain where systems have undergone an industrial transformationDiscusses current research of machine learning along with blockchain techniques that can fill the gap between research and industrial exposureCovers the effects that the 4th Industrial Revolution has on industrial infrastructuresLooks at industry change patterns and innovations that are speeding up industrial transformation activities

Integration of AI-Based Manufacturing and Industrial Engineering Systems with the Internet of Things describes how AI techniques, such as deep learning, cognitive computing, and Machine Learning, can be used to analyze massive volumes of data produced by IoT devices in manufacturing environments.The potential benefits and challenges associated with the integration of AI and IoT in industrial environments are explored throughout the book as the authors delve into various aspects of the integration process. The role of IoT-enabled sensors, actuators, and smart devices in capturing real-time data from manufacturing processes, supply chains, and equipment is discussed along with how data can be processed and analyzed using AI algorithms to derive actionable insights, optimize production, improve quality control, and enhance overall operational efficiency.A valuable resource for researchers, practitioners, and professionals involved in the fields of AI, IoT, manufacturing systems, and industrial engineering, and combines theoretical foundations, practical applications, and case studies.

Integration of AI-Based Manufacturing and Industrial Engineering Systems with the Internet of Things describes how AI techniques, such as deep learning, cognitive computing, and Machine Learning, can be used to analyze massive volumes of data produced by IoT devices in manufacturing environments.The potential benefits and challenges associated with the integration of AI and IoT in industrial environments are explored throughout the book as the authors delve into various aspects of the integration process. The role of IoT-enabled sensors, actuators, and smart devices in capturing real-time data from manufacturing processes, supply chains, and equipment is discussed along with how data can be processed and analyzed using AI algorithms to derive actionable insights, optimize production, improve quality control, and enhance overall operational efficiency.A valuable resource for researchers, practitioners, and professionals involved in the fields of AI, IoT, manufacturing systems, and industrial engineering, and combines theoretical foundations, practical applications, and case studies.

Technology Innovation Pillars for Industry 4.0: Challenges, Improvements, and Case Studies discusses the latest innovations in the application of technologies to Industry 4.0 and the nine pillars and how they relate, support, and bridge the gap between the digital and physical worlds we now live in.This book discusses each of the nine pillars and the roles they play in the rapid transformation of the design and operation, and offers applications and case studies supporting Industry 4.0 technologies. It presents the supply chain organizational activities utilizing cyber- physical systems architectures and talks about the advantages of intelligent manufacturing and the ability to proactively detect and respond to events, to improve quality and yield, reduce downtime, and lead to better overall equipment effectiveness among other advantages in smart factory operations.This reference book provides a great resource for undergraduate and graduate students, industrial and manufacturing engineers, and engineers of related disciplines along with business professionals, explaining what the nine pillars are and how they relate to Industry 4.0 and smart factories.

Technology Innovation Pillars for Industry 4.0: Challenges, Improvements, and Case Studies discusses the latest innovations in the application of technologies to Industry 4.0 and the nine pillars and how they relate, support, and bridge the gap between the digital and physical worlds we now live in.This book discusses each of the nine pillars and the roles they play in the rapid transformation of the design and operation, and offers applications and case studies supporting Industry 4.0 technologies. It presents the supply chain organizational activities utilizing cyber- physical systems architectures and talks about the advantages of intelligent manufacturing and the ability to proactively detect and respond to events, to improve quality and yield, reduce downtime, and lead to better overall equipment effectiveness among other advantages in smart factory operations.This reference book provides a great resource for undergraduate and graduate students, industrial and manufacturing engineers, and engineers of related disciplines along with business professionals, explaining what the nine pillars are and how they relate to Industry 4.0 and smart factories.

This book presents the role of AI-Driven Digital Twin in the Industry 4.0 ecosystem by focusing on Smart Manufacturing, sustainable development, and many other applications. It also discusses different case studies and presents an in-depth understanding of the benefits and limitations of using AI and Digital Twin for industrial developments.AI-Driven Digital Twin and Industry 4.0: A Conceptual Framework with Applications introduces the role of Digital Twin in Smart Manufacturing and focuses on the Digital Twin framework throughout. It provides a summary of the various AI applications in the Industry 4.0 environment and emphasizes the role of advanced computational and communication technologies. The book offers demonstrative examples of AI-Driven Digital Twin in various application domains and includes AI techniques used to analyze the environmental impact of industrial operations along with examples. The book reviews the major challenges in the deployment of AI-Driven Digital Twin in the Industry 4.0 ecosystem and presents an understanding of how AI is used in the designing of Digital Twin for various applications. The book also enables familiarity with various industrial applications of computational and communication technologies and summarizes the ongoing research and innovations in the areas of AI, Digital Twin, and Smart Manufacturing while also tracking the various research challenges along with future advances.This reference book is a must-read and is very beneficial to students, researchers, academicians, industry experts, and professionals working in related fields.

This book presents the role of AI-Driven Digital Twin in the Industry 4.0 ecosystem by focusing on Smart Manufacturing, sustainable development, and many other applications. It also discusses different case studies and presents an in-depth understanding of the benefits and limitations of using AI and Digital Twin for industrial developments.AI-Driven Digital Twin and Industry 4.0: A Conceptual Framework with Applications introduces the role of Digital Twin in Smart Manufacturing and focuses on the Digital Twin framework throughout. It provides a summary of the various AI applications in the Industry 4.0 environment and emphasizes the role of advanced computational and communication technologies. The book offers demonstrative examples of AI-Driven Digital Twin in various application domains and includes AI techniques used to analyze the environmental impact of industrial operations along with examples. The book reviews the major challenges in the deployment of AI-Driven Digital Twin in the Industry 4.0 ecosystem and presents an understanding of how AI is used in the designing of Digital Twin for various applications. The book also enables familiarity with various industrial applications of computational and communication technologies and summarizes the ongoing research and innovations in the areas of AI, Digital Twin, and Smart Manufacturing while also tracking the various research challenges along with future advances.This reference book is a must-read and is very beneficial to students, researchers, academicians, industry experts, and professionals working in related fields.

Artificial intelligence (AI) and mechatronics are booming areas where most of the industrial sectors are becoming smart nowadays. This handbook includes material of multidisciplinary content from the AI, mechanical, and electronics engineering domains, among others. It gives insights into various application sectors discussing current global developments in mechatronics employing AI technology and addressing the complexity of current issues and the effects of diverse mechatronics systems.Handbook of AI-Based Mechatronics Systems and Smart Solutions in Industrial Automation focuses on system automation, predictive analysis, preventive analysis, and real-time decision-making systems for next-generation automation. It discusses the advancements of mechatronics systems using AI applications along with the global approach toward smart industrial automation and presents the impact of AI on today’s work of autonomous and industrial automation. The book discusses future research potential and is beneficial to manufacturing, healthcare, and finance disputes, while it offers AI algorithms to analyze large amounts of data and identifies patterns, trends, and anomalies for accurate predictions and optimization processes. The handbook also addresses use cases and case studies related to AI in mechatronics along with applications.Scholars in the field of AI in mechatronics and related applications will find this book useful. In particular, attention is drawn to both fundamental ideas and important practical contexts. Readers interested in the most recent findings in the field of problem-oriented processing approaches in mechatronics, including those in academia, data science, industry, research, and graduate and undergraduate students, will find this fascinating handbook extremely interesting.

The use of intelligence in manufacturing has emerged as a fascinating subject for academics and businesses everywhere. This book focuses on various manufacturing operations and services which are provided to customers to achieve greater manufacturing flexibility, as well as widespread customization and improved quality with the help of advanced and smart technologies. It describes cyber-physical systems and the whole product life cycle along with a variety of smart sensors, adaptive decision models, high-end materials, smart devices, and data analytics.Intelligent Manufacturing and Industry 4.0: Impact, Trends, and Opportunities focuses on Intelligent Manufacturing and the design of smart devices and products that meet the demand of Industry 4.0, manufacturing and cyber-physical systems, along with real-time data analytics for Intelligent Manufacturing. The usage of advanced smart and sensing technologies in Intelligent Manufacturing for healthcare solutions is discussed as well. Popular use cases and case studies related to Intelligent Manufacturing are addressed to provide a better understanding of this topic.This publication is ideally designed for use by technology development practitioners, academicians, data scientists, industry professionals, researchers, and students interested in uncovering the latest innovations in the field of Intelligent Manufacturing.Features: Presents cutting-edge manufacturing technologies and information to maximise product exchanges and productionDiscusses the improvement in service quality, product quality, and production effectivenessConveys how a manufacturing company's competitiveness can increase if it can manage the turbulence and changes in the global marketPresents how intelligence production is essential in Industry 4.0 and how Industry 4.0 offers greater manufacturing flexibility, as well as widespread customisation, improved quality, and increased productivityCovers the ways businesses handle the challenges of generating an increasing number of customised items with quick time to market and greater qualityIncludes popular use cases and case studies related to intelligent manufacturing to provide a better understanding of this discipline

The use of intelligence in manufacturing has emerged as a fascinating subject for academics and businesses everywhere. This book focuses on various manufacturing operations and services which are provided to customers to achieve greater manufacturing flexibility, as well as widespread customization and improved quality with the help of advanced and smart technologies. It describes cyber-physical systems and the whole product life cycle along with a variety of smart sensors, adaptive decision models, high-end materials, smart devices, and data analytics.Intelligent Manufacturing and Industry 4.0: Impact, Trends, and Opportunities focuses on Intelligent Manufacturing and the design of smart devices and products that meet the demand of Industry 4.0, manufacturing and cyber-physical systems, along with real-time data analytics for Intelligent Manufacturing. The usage of advanced smart and sensing technologies in Intelligent Manufacturing for healthcare solutions is discussed as well. Popular use cases and case studies related to Intelligent Manufacturing are addressed to provide a better understanding of this topic.This publication is ideally designed for use by technology development practitioners, academicians, data scientists, industry professionals, researchers, and students interested in uncovering the latest innovations in the field of Intelligent Manufacturing.Features: Presents cutting-edge manufacturing technologies and information to maximise product exchanges and productionDiscusses the improvement in service quality, product quality, and production effectivenessConveys how a manufacturing company's competitiveness can increase if it can manage the turbulence and changes in the global marketPresents how intelligence production is essential in Industry 4.0 and how Industry 4.0 offers greater manufacturing flexibility, as well as widespread customisation, improved quality, and increased productivityCovers the ways businesses handle the challenges of generating an increasing number of customised items with quick time to market and greater qualityIncludes popular use cases and case studies related to intelligent manufacturing to provide a better understanding of this discipline

The field of industrial engineering has undergone a transformation in recent years, driven by emerging technologies such as Artificial Intelligence (AI), Machine Learning, and the Internet of Things (IoT). To keep pace with these advancements, a shift toward AI-driven development has become a necessity.From case studies to national and international best practices, Intelligent Manufacturing: Exploring AI, Blockchain, and Smart Technologies in Industry 4.0 delves into the challenges of incorporating AI-driven frameworks and standards into the smart industry landscape. It explores the integration of these advanced technologies into Industry 4.0 and presents a comprehensive overview of their potential impact on industrial processes. Topics covered include security management, standard specifications, and sustainable manufacturing, highlighting the latest innovations in industrial engineering design. The book goes on to discuss the practical applications of AI-driven technologies in manufacturing, such as simulation, optimization, and sustainable development of industrial processes. A variety of real-world examples are offered, and a discussion of how these technologies are being deployed in different industries provides a clear understanding of their applications and benefits.This book is an essential guide for students, researchers, and industry professionals seeking to understand the evolving landscape of intelligent manufacturing and industrial engineering.

The field of industrial engineering has undergone a transformation in recent years, driven by emerging technologies such as Artificial Intelligence (AI), Machine Learning, and the Internet of Things (IoT). To keep pace with these advancements, a shift toward AI-driven development has become a necessity.From case studies to national and international best practices, Intelligent Manufacturing: Exploring AI, Blockchain, and Smart Technologies in Industry 4.0 delves into the challenges of incorporating AI-driven frameworks and standards into the smart industry landscape. It explores the integration of these advanced technologies into Industry 4.0 and presents a comprehensive overview of their potential impact on industrial processes. Topics covered include security management, standard specifications, and sustainable manufacturing, highlighting the latest innovations in industrial engineering design. The book goes on to discuss the practical applications of AI-driven technologies in manufacturing, such as simulation, optimization, and sustainable development of industrial processes. A variety of real-world examples are offered, and a discussion of how these technologies are being deployed in different industries provides a clear understanding of their applications and benefits.This book is an essential guide for students, researchers, and industry professionals seeking to understand the evolving landscape of intelligent manufacturing and industrial engineering.

By enabling the conversion of traditional manufacturing systems into contemporary digitalized ones, Internet of Things (IoT) adoption in manufacturing creates huge economic prospects through reshaping industries. Modern businesses can more readily implement new data-driven strategies and deal with the pressure of international competition thanks to Industrial IoT. But as the use of IoT grows, the amount of created data rises, turning industrial data into Industrial Big Data.Internet of Things and Big Data Analytics-Based Manufacturing shows how Industrial Big Data can be produced as a result of IoT usage in manufacturing, considering sensing systems and mobile devices. Different IoT applications that have been developed are demonstrated and it is shown how genuine industrial data can be produced, leading to Industrial Big Data. This book is organized into four sections discussing IoT and technology, the future of Big Data, algorithms, and case studies demonstrating the use of IoT and Big Data in a variety of industries, including automation, industrial manufacturing, and healthcare.This reference title brings all related technologies into a single source so that researchers, undergraduate and postgraduate students, academicians, and those in the industry can easily understand the topic and further their knowledge.

By enabling the conversion of traditional manufacturing systems into contemporary digitalized ones, Internet of Things (IoT) adoption in manufacturing creates huge economic prospects through reshaping industries. Modern businesses can more readily implement new data-driven strategies and deal with the pressure of international competition thanks to Industrial IoT. But as the use of IoT grows, the amount of created data rises, turning industrial data into Industrial Big Data.Internet of Things and Big Data Analytics-Based Manufacturing shows how Industrial Big Data can be produced as a result of IoT usage in manufacturing, considering sensing systems and mobile devices. Different IoT applications that have been developed are demonstrated and it is shown how genuine industrial data can be produced, leading to Industrial Big Data. This book is organized into four sections discussing IoT and technology, the future of Big Data, algorithms, and case studies demonstrating the use of IoT and Big Data in a variety of industries, including automation, industrial manufacturing, and healthcare.This reference title brings all related technologies into a single source so that researchers, undergraduate and postgraduate students, academicians, and those in the industry can easily understand the topic and further their knowledge.

The industrial landscape is changing rapidly, and so is global society. This change is driven by the growing adoption of the Industrial Internet of Things (IIoT) and artificial intelligence (AI) technologies. IIoT and AI are transforming the way industrial engineering is done, enabling new levels of automation, productivity, and efficiency. However, as IIoT and AI become more pervasive in the industrial world, they also offer new security risks that must be addressed to ensure the reliability and safety of critical systems.Industrial Internet of Things Security: Protecting AI-Enabled Engineering Systems in Cloud and Edge Environments provides a comprehensive guide to IIoT security, covering topics such as network architecture, risk management, data security, and compliance. It addresses the unique security challenges that the cloud and edge environments pose, providing practical guidance for securing IIoT networks in these contexts. It includes numerous real-world case studies and examples, providing readers with practical insights into how IIoT security and AI-enabled industrial engineering are being implemented in various industries. Best practices are emphasized for the readers to ensure the reliability, safety, and security of their systems while also learning the latest developments in IIoT security for AI-enabled industrial engineering systems in this rapidly evolving field.By offering step-by-step guidance for the implantation process along with best practices, this book becomes a valuable resource for practitioners and engineers in the areas of industrial engineering, IT, computer engineering, and anyone looking to secure their IIoT network against cyber threats.

The industrial landscape is changing rapidly, and so is global society. This change is driven by the growing adoption of the Industrial Internet of Things (IIoT) and artificial intelligence (AI) technologies. IIoT and AI are transforming the way industrial engineering is done, enabling new levels of automation, productivity, and efficiency. However, as IIoT and AI become more pervasive in the industrial world, they also offer new security risks that must be addressed to ensure the reliability and safety of critical systems.Industrial Internet of Things Security: Protecting AI-Enabled Engineering Systems in Cloud and Edge Environments provides a comprehensive guide to IIoT security, covering topics such as network architecture, risk management, data security, and compliance. It addresses the unique security challenges that the cloud and edge environments pose, providing practical guidance for securing IIoT networks in these contexts. It includes numerous real-world case studies and examples, providing readers with practical insights into how IIoT security and AI-enabled industrial engineering are being implemented in various industries. Best practices are emphasized for the readers to ensure the reliability, safety, and security of their systems while also learning the latest developments in IIoT security for AI-enabled industrial engineering systems in this rapidly evolving field.By offering step-by-step guidance for the implantation process along with best practices, this book becomes a valuable resource for practitioners and engineers in the areas of industrial engineering, IT, computer engineering, and anyone looking to secure their IIoT network against cyber threats.

This book revolves around the Gig Economy and its multifaceted implications. It takes a broad view by examining the opportunities and challenges posed by the interplay between Smart Industries and the Gig Economy.Smart Industries in the Gig Economy: Scope, Opportunities, and Challenges discusses topics from manufacturing to service sectors, from startups to established corporations, and from localized operations to global networks using the threads of Smart Industries and the Gig Economy to weave a tapestry of innovation and change. It provides a panoramic view of Smart Industries in the context of the Gig Economy and emphasizes the recent cutting-edge technologies like Robotics, Artificial Intelligence, the Internet of things, Quantum Technology, Semiconductors, Additive Manufacturing, etc. The book focuses on the intervention of the Gig Economy in major cutting-edge technologies of the global industrial revolution working towards automation and its impact on enhancing smart industries.Written as a reference for students, scholars, lecturers, researchers, scientists, experts, specialists, and engineers, this book represents an area of significant interest and importance in the realm of smart industries and the Gig Economy.

Federated machine learning is a novel approach to combining distributed machine learning, cryptography, security, and incentive mechanism design. It allows organizations to keep sensitive and private data on users or customers decentralized and secure, helping them comply with stringent data protection regulations like GDPR and CCPA.Artificial Intelligence Using Federated Learning: Fundamentals, Challenges, and Applications enables training AI models on a large number of decentralized devices or servers, making it a scalable and efficient solution. It also allows organizations to create more versatile AI models by training them on data from diverse sources or domains. This approach can unlock innovative use cases in fields like healthcare, finance, and IoT, where data privacy is paramount.The book is designed for researchers working in Intelligent Federated Learning and its related applications, as well as technology development, and is also of interest to academicians, data scientists, industrial professionals, researchers, and students.

Federated machine learning is a novel approach to combining distributed machine learning, cryptography, security, and incentive mechanism design. It allows organizations to keep sensitive and private data on users or customers decentralized and secure, helping them comply with stringent data protection regulations like GDPR and CCPA.Artificial Intelligence Using Federated Learning: Fundamentals, Challenges, and Applications enables training AI models on a large number of decentralized devices or servers, making it a scalable and efficient solution. It also allows organizations to create more versatile AI models by training them on data from diverse sources or domains. This approach can unlock innovative use cases in fields like healthcare, finance, and IoT, where data privacy is paramount.The book is designed for researchers working in Intelligent Federated Learning and its related applications, as well as technology development, and is also of interest to academicians, data scientists, industrial professionals, researchers, and students.

The rapid convergence of the industrial internet of things (IIoT) and machine learning (ML) is propelling us into a new era known as Industry 5.0. This era is defined by unparalleled levels of automation, customization, and sustainability. Despite these advancements, there is a noticeable absence of comprehensive resources that offer a complete understanding of these transformative technologies and their practical applications across various industrial sectors. Transforing Industries: Capturing the Potential of IIoT and ML in the Era of Industry 5.0 aims to fill the void by providing a timely and relevant guide that explores the theoretical foundations of the technologies while also presenting real-world case studies and best practices.This comprehensive text discusses the profound impact of the IIoT and ML on various sectors, including manufacturing, supply chain management, and energy production. It provides a thorough examination of the opportunities and obstacles associated with these cutting-edge technologies. Through real-world case studies and success stories, readers gain insight into how industry leaders have successfully leveraged IIoT and ML solutions to optimize operational efficiency, foster innovation, and achieve unparalleled excellence. Practical strategies and detailed guidelines are also offered to facilitate the seamless integration of these technologies into existing workflows, empowering businesses to navigate the complexities of digital transformation with confidence. The focus of this publication also extends to future trends and potential disruptors in the era of Industry 5.0, equipping readers with the knowledge needed to anticipate and adapt to emerging challenges. Furthermore, the exploration of how IIoT and ML can streamline resource allocation, minimize waste, and promote sustainable practices underscores the alignment of technological advancements with corporate, environmental, and social responsibilities.By equipping industry leaders, professionals, and entrepreneurs with actionable insights, this book will empower businesses to stay ahead of the curve, foster innovation, optimize resource utilization, and ultimately gain a competitive advantage in the ever-evolving manufacturing landscape.

In an era where healthcare innovation and technological advancement converge at unprecedented speed, the integration of intelligent manufacturing into medical device production represents one of the most promising frontiers in modern healthcare. The Handbook of Smart Healthcare: Integrating Intelligent Manufacturing in Medical Device Production arrives at a critical juncture in the collective journey toward more efficient, precise, and patient-centered healthcare solutions.This handbook explores the multifaceted landscape of intelligent manufacturing as it applies specifically to medical device production. From regulatory considerations unique to healthcare to the implementation of digital twins for virtual testing, from quality assurance protocols enhanced by machine learning to supply chain optimization through predictive analytics, this handbook provides comprehensive insights into both theoretical frameworks and practical applications.Whether readers are an engineer seeking to implement advanced manufacturing techniques, a healthcare administrator evaluating new technologies, a regulatory professional navigating compliance in an evolving landscape, or a student preparing to enter this dynamic field, this handbook will serve as an invaluable resource in their professional journey.