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9781394303571 |
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9781394303601 |
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electronic book |
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1394303602 |
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electronic book |
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electronic book |
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electronic book |
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hardcover |
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1394303572 |
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hardcover |
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10.1002/9781394303601 |
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doi |
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CIPO000143159 |
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(OCoLC)1454571739 |
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9781394303571 |
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O'Reilly Media |
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10955397 |
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IEEE |
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eng |
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MAIN |
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TS155.63 |
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.A78 2024 |
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23 |
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003.3 |
| 245 00 - TITLE STATEMENT |
|---|
| Title |
Artificial intelligence-enabled digital twin for smart manufacturing / |
| Statement of responsibility, etc |
edited by Amit Kumar Tyagi, Shrikant Tiwari, Senthil Kumar Arumugam and Avinash Kumar Sharma. |
| 264 #1 - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Place of publication, distribution, etc |
Hoboken, New Jersey : |
| Name of publisher, distributor, etc |
John Wiley & Sons, Inc. ; |
| Place of publication, distribution, etc |
Beverly, MA : |
| Name of publisher, distributor, etc |
Scrivener Publishing LLC, |
| Date of publication, distribution, etc |
2024. |
| 264 #4 - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
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©2024 |
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1 online resource |
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text |
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txt |
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rdacontent |
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computer |
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rdamedia |
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online resource |
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cr |
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rdacarrier |
| 505 0# - CONTENTS |
|---|
| Formatted contents note |
Cover -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- Part 1: Fundamentals of AI-Based Smart Manufacturing -- Chapter 1: Machine Learning Fundamentals -- 1.1 Introduction -- 1.2 Classification -- 1.2.1 Linear Model -- 1.2.1.1 Logistic Regression -- 1.2.1.2 Support Vector Machine -- 1.2.2 Nonlinear Model -- 1.2.2.1 K-Nearest Neighbor -- 1.2.2.2 Naive Bayes -- 1.2.2.3 Decision Tree -- 1.2.2.4 Random Forest -- 1.3 Regression -- 1.3.1 Linear Regression -- 1.3.2 Multiple Linear Regression -- 1.3.3 Logistic Regression -- 1.3.3.1 Three Types of Logistic Regression -- 1.3.4 Polynomial Regression -- 1.3.5 Support Vector Regression -- 1.3.6 Decision Tree Regression -- 1.3.7 Random Forest Regression -- 1.3.7.1 Random Forest Algorithm in Practice -- 1.3.7.2 Random Forest Algorithm Application Examples -- 1.3.8 Lasso Regression -- 1.4 Clustering -- 1.4.1 Clustering Algorithms -- 1.4.2 K-Means Clustering Algorithm -- 1.4.3 Mean Shift Clustering Algorithm -- 1.4.4 DBSCAN Clustering -- 1.4.4.1 Two Elements Required by DBSCAN -- 1.4.4.2 Steps followed to do DBSCAN Algorithm -- 1.5 Conclusion -- References -- Chapter 2: Industry 4.0 in Manufacturing, Communication, Transportation, Healthcare -- 2.1 Introduction -- 2.1.1 The Significance of Industry 4.0 Across Multiple Domains -- 2.2 Industry 4.0 in Manufacturing: Overview -- 2.2.1 Benefits of Industry 4.0 in Manufacturing -- 2.2.2 Examples of Industry 4.0 in Manufacturing -- 2.2.3 Challenges in Implementation of Industry 4.0 in Manufacturing -- 2.3 Industry 4.0 in Communication: Overview -- 2.3.1 Benefits of Industry 4.0 in Communication -- 2.3.2 Examples of Industry 4.0 in Communication -- 2.3.3 Challenges of Implementing Industry 4.0 in Communication -- 2.4 Industry 4.0 in Transportation: Overview -- 2.4.1 Industry 4.0’s Advantages for Transportation. |
| 505 8# - CONTENTS |
|---|
| Formatted contents note |
2.4.2 Application of Industry 4.0 in Transportation -- 2.4.3 Challenges of Implementing Industry 4.0 in Transportation -- 2.5 Industry 4.0 in Healthcare: Overview -- 2.5.1 Benefits of Industry 4.0 in Healthcare -- 2.5.2 Healthcare Professional Applications -- 2.5.3 Challenges of Implementing Industry 4.0 in Healthcare -- 2.6 Future of Industry 4.0 in Terms of Emerging Trends and Technologies -- 2.7 Implications of Industry 4.0 on Various Sectors -- 2.8 Opportunities for Businesses and Industries -- 2.8.1 Challenges for Businesses and Industries -- 2.9 Conclusion -- References -- Chapter 3: Data Analytics and Big Data Analytics -- 3.1 Introduction to Data Analytics -- 3.1.1 Types of Data Analytics Techniques -- 3.1.2 Distinction between Big Data, Data Analytics, and Data Science -- 3.2 Literature Survey -- 3.3 An Overview of Big Data Analytics -- 3.3.1 Difference between Big Data and Data Analytics -- 3.3.2 The Three Vs of Big Data -- 3.3.3 Big Data Analytics’ Advantages and Benefits -- 3.3.4 Steps for Big Data Analytics -- 3.4 Process of Generation of Big Data Analytics for Manufacturing -- 3.4.1 Big Data Analysis’s Function in Manufacturing -- 3.4.2 Several of the Most Well-Known Actual Applications of Big Data in Manufacturing -- 3.4.3 Utilizing Big Data in the Manufacturing Industry -- 3.5 Utilizing Big Data Analytics for Manufacturing Market Analysis -- 3.6 Global Big Data Insights for the Manufacturing Sector -- 3.7 Conclusion -- References -- Chapter 4: Artificial Intelligence Empowered Smart Manufacturing for Modern Society: A Review -- 4.1 Introduction to AI, Smart Manufacturing -- 4.1.1 Challenges and Opportunities -- 4.2 AI Applications in Manufacturing -- 4.3 Benefits and Challenges with AI -- 4.4 Emerging Technologies Enabling Smart Manufacturing -- 4.4.1 Internet of Things (IoT) in Smart Manufacturing. |
| 505 8# - CONTENTS |
|---|
| Formatted contents note |
4.4.2 Big Data and Analytics in Smart Manufacturing -- 4.4.3 Robotics and Automation in Smart Manufacturing -- 4.4.4 AI-IoT-Cloud in Smart Manufacturing -- 4.4.5 AI Blockchain-Based Smart Manufacturing -- 4.5 AI-Driven Smart Manufacturing -- 4.5.1 Predictive Maintenance and Quality Control in Smart Manufacturing -- 4.5.2 Humanâ€"Machine Collaboration -- 4.6 Popular Challenges and Issues Towards AI-Based Smart Manufacturing Systems -- 4.7 AI-Based Smart Manufacturing Systems for the Future -- 4.7.1 AI-Enhanced Supply Chain Management for the Future -- 4.7.2 Smart Factory Concepts for Next Generation -- 4.7.3 Quality Assurance and Inspection in Real Time -- 4.8 Improving Operational Efficiency and Environmental Sustainability in AI Based Smart Manufacturing -- 4.8.1 Cost Reduction and Resource Optimization in AI-Based Smart Manufacturing -- 4.9 Future Research Opportunities and Research Gaps Towards AI-Empowered Smart Manufacturing -- 4.10 The Evolution of Industry 5.0 and Industry 6.0 -- 4.11 Conclusion -- References -- Chapter 5: Use Cases of Digital Twin in Smart Manufacturing -- 5.1 Introduction -- 5.1.1 Smart Manufacturing Streams Utilizing Digital Twin Technology -- 5.1.2 Technological Advancement That Makes Digital Twins Ideal for Smart Manufacturing System Design -- 5.2 Review of Relevant Literature -- 5.3 Various Use Cases of Digital Twin in Smart Manufacturing -- 5.3.1 Data-Driven Smart Factory -- 5.3.2 Cyber-Physical System (CPS) Integration -- 5.3.3 Human-Robot Collaboration -- 5.3.4 Adaptive Federated Learning for Industrial IoT -- 5.4 Information Management System-Based Digital Twins and Big Data for Sustainable Smart Manufacturing -- 5.5 Challenges and Future Avenues -- 5.6 Conclusion -- References -- Part 2: Methods and Applications -- Chapter 6: Distributed Systems and Distributed Ledger Technology -- An Introduction. |
| 505 8# - CONTENTS |
|---|
| Formatted contents note |
6.1 An Introduction -- 6.2 Related Work -- 6.3 Blockchain â€" In General -- 6.4 Evolution of Blockchain -- 6.4.1 Progress of Blockchain 1.0 to Blockchain 4.0 -- 6.5 Generic Elements of a Blockchain -- 6.6 Benefits and Limitations of Blockchain -- 6.7 Tiers of Blockchain Technology -- 6.8 Features of a Blockchain -- 6.9 Types of Blockchain -- 6.10 Open Issues in Blockchain Technology -- 6.11 Important Challenges with Blockchain Technology -- 6.12 Conclusion -- References -- Chapter 7: Digital Twins Tools and Technologies in Smart Manufacturing -- 7.1 Introduction -- 7.2 Applications and Characteristics of DT -- 7.3 DT in Manufacturing -- 7.4 Related Work -- 7.5 Case Study: Challenge Advisory -- 7.5.1 Case Study: Whirlpool -- 7.5.2 Case Study: Woodward -- 7.6 Challenges to Implement DT -- 7.6.1 Innovation of Technology -- 7.6.2 Time and Cost -- 7.6.3 Lack of Standards and Regulations -- 7.6.4 Data Related Issues -- 7.6.5 Life-Cycle Mismatching -- 7.7 Open Research -- 7.7.1 Integration with the Internet of Things (IoT) -- 7.7.2 Integration with Industry 4.0 Technologies -- 7.7.3 Multi-Scale DTs -- 7.7.4 Integration with Advanced Analytics -- 7.7.5 Real-Time Optimization -- 7.7.6 Collaboration and Communication -- 7.7.7 Optimization of Supply Chain Processes -- 7.7.8 Humanâ€"Machine Collaboration -- 7.7.9 Cybersecurity -- 7.7.10 Scalability -- 7.8 Conclusion -- References -- Chapter 8: Blockchain Based Digital Twin for Smart Manufacturing -- 8.1 Introduction to Blockchain, Digital Twin, and Smart Manufacturing -- 8.1.1 Background of Blockchain, Digital Twin, and Smart Manufacturing -- 8.1.2 Role of Digital Twins in Manufacturing -- 8.1.3 The Role of Blockchain in Smart Manufacturing -- 8.1.4 Organization of the Work -- 8.2 Issues and Challenges in Conventional Manufacturing Processes -- 8.2.1 Need for Digital Transformation in Manufacturing. |
| 505 8# - CONTENTS |
|---|
| Formatted contents note |
8.3 Digital Twins and Blockchain in Manufacturing -- 8.3.1 Digital Twin, Types, and Applications of Digital Twins in Manufacturing -- 8.3.2 Benefits and Key Use Cases of Digital Twin in Manufacturing -- 8.3.3 Blockchain Components: Blocks, Transactions, and Smart Contracts in Smart Manufacturing -- 8.3.4 Security and Data Integrity Issues in Implementing Digital Twin and Blockchain in Smart Manufacturing -- 8.4 Synergy of Blockchain and Digital Twins for Smart Manufacturing -- 8.5 AI, Blockchain, IoT and Other Emerging Technologies: Role in Smart Manufacturing -- 8.6 Key Technologies for Blockchain-Based Digital Twins -- 8.6.1 Integration of Emerging Technologies with Existing Manufacturing Systems -- 8.7 Applications of Blockchain-Based Digital Twins in Smart Manufacturing -- 8.8 Security and Data Privacy in Smart Manufacturing -- In General -- 8.8.1 Cyber Security in the Smart Factory -- 8.9 Case Studies -- 8.9.1 Blockchain for Smart Manufacturing in the Automotive Industry -- 8.9.2 Digital Twins in Pharmaceutical Manufacturing -- 8.9.3 Blockchain and Digital Twin in Aerospace Manufacturing -- 8.10 Future Research Towards Integration of AI and Blockchain for Autonomous Manufacturing -- 8.11 Sustainability and Environmental Impact via Smart Manufacturing -- 8.12 Conclusion -- References -- Chapter 9: Blockchain for Internet of Things and Machine Learning-Based Automated Sectors -- 9.1 Introduction -- 9.2 Evolution Variants and Architecture of Internet of Things -- 9.3 Evolution, Variants, and Architecture Machine Learning -- 9.4 Blockchain for Internet of Things and Machine Learning -- 9.5 Blockchain-Based Learning Automated Analytics Platforms -- 9.6 Blockchain Inclusion in Internet of Things Architecture and Machine Learning -- 9.7 Features Benefits Limitations Applications and Challenges of Internet of Things. |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc |
An essential book on the applications of AI and digital twin technology in the smart manufacturing sector. In the rapidly evolving landscape of modern manufacturing, the integration of cutting-edge technologies has become imperative for businesses to remain competitive and adaptive. Among these technologies, Artificial Intelligence (AI) stands out as a transformative force, revolutionizing traditional manufacturing processes and making the way for the era of smart manufacturing. At the heart of this technological revolution lies the concept of the Digital Twin--an innovative approach that bridges the physical and digital realms of manufacturing. By creating a virtual representation of physical assets, processes, and systems, organizations can gain unprecedented insights, optimize operations, and enhance decision-making capabilities. This timely book explores the convergence of AI and Digital Twin technologies to empower smart manufacturing initiatives. Through a comprehensive examination of principles, methodologies, and practical applications, it explains the transformative potential of AI-enabled Digital Twins across various facets of the manufacturing lifecycle. From design and prototyping to production and maintenance, AI-enabled Digital Twins offer multifaceted advantages that redefine traditional paradigms. By leveraging AI algorithms for data analysis, predictive modeling, and autonomous optimization, manufacturers can achieve unparalleled levels of efficiency, quality, and agility. This book explains how AI enhances the capabilities of Digital Twins by creating a powerful tool that can optimize production processes, improve product quality, and streamline operations. Note that the Digital Twin in this context is a virtual representation of a physical manufacturing system, including machines, processes, and products. It continuously collects real-time data from sensors and other sources, allowing it to mirror the physical system's behavior and performance. What sets this Digital Twin apart is the incorporation of AI algorithms and machine learning techniques that enable it to analyze and predict outcomes, recommend improvements, and autonomously make adjustments to enhance manufacturing efficiency. This book outlines essential elements, like real-time monitoring of machines, predictive analytics of machines and data, optimization of the resources, quality control of the product, resource management, decision support (timely or quickly accurate decisions). Moreover, this book elucidates the symbiotic relationship between AI and Digital Twins, highlighting how AI augments the capabilities of Digital Twins by infusing them with intelligence, adaptability, and autonomy. Hence, this book promises to enhance competitiveness, reduce operational costs, and facilitate innovation in the manufacturing industry. By harnessing AI's capabilities in conjunction with Digital Twins, manufacturers can achieve a more agile and responsive production environment, ultimately driving the evolution of smart factories and Industry 4.0/5.0. Audience This book has a wide audience in computer science, artificial intelligence, and manufacturing engineering, as well as engineers in a variety of industrial manufacturing industries. It will also appeal to economists and policymakers working on the circular economy, clean tech investors, industrial decision-makers, and environmental professionals. |
| 588 ## - SOURCE OF DESCRIPTION NOTE |
|---|
| Source of description note |
Description based on online resource; title from digital title page (viewed on October 15, 2024). |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Computer integrated manufacturing systems. |
| Authority record control number |
http://id.loc.gov/authorities/subjects/sh85029506 |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Digital twins (Computer simulation) |
| General subdivision |
Industrial applications. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Artificial intelligence. |
| Authority record control number |
http://id.loc.gov/authorities/subjects/sh85008180 |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Manufacturing processes |
| General subdivision |
Automation. |
| Authority record control number |
http://id.loc.gov/authorities/subjects/sh88004818 |
| 655 #0 - INDEX TERM--GENRE/FORM |
|---|
| Genre/form data or focus term |
Electronic books. |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Tyagi, Amit Kumar, |
| Relator term |
editor. |
| Real World Object URI |
https://id.oclc.org/worldcat/entity/E39PCjHGb3V4Yr7PtVCgfBbMHd |
| Authority record control number |
http://id.loc.gov/authorities/names/no00080939 |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Tiwari, Shrikant, |
| Relator term |
editor. |
| Authority record control number |
http://id.loc.gov/authorities/names/n2024036730 |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Arumugam, Senthil Kumar, |
| Relator term |
editor. |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Sharma, Avinash Kumar, |
| Dates associated with a name |
1982- |
| Relator term |
editor. |
| Authority record control number |
http://id.loc.gov/authorities/names/n2023034588 |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY |
|---|
| Display text |
Print version: |
| International Standard Book Number |
1394303572 |
| -- |
9781394303571 |
| Record control number |
(OCoLC)1450833371 |
| 856 40 - ELECTRONIC LOCATION AND ACCESS |
|---|
| Uniform Resource Identifier |
https://onlinelibrary.wiley.com/doi/book/10.1002/9781394303601 |
| Link text |
Full text is available at Wiley Online Library. Click here to view. |
| 942 ## - ADDED ENTRY ELEMENTS |
|---|
| Source of classification or shelving scheme |
|
| Item type |
EBOOK |