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Submit a ProposalSustainable and Generative Artificial Intelligence in Manufacturing
One Line Description
Unlock the future of green production with this indispensable guide, bridging cutting-edge generative AI and practical industrial frameworks to build more efficient, eco-friendly, and ethically sound manufacturing systems.
Description
The integration of artificial intelligence in manufacturing is transforming industries by optimizing production, reducing waste, and improving efficiency. However, as industries increasingly adopt AI-driven processes, concerns about sustainability and ethical AI deployment have emerged. This book explores the critical intersection of AI, sustainability, and generative AI technologies to advance responsible manufacturing practices. It provides an in-depth analysis of how AI can be leveraged to create eco-friendly, energy-efficient, and resource-conscious manufacturing systems while ensuring long-term viability and minimal environmental impact. The book adopts a balanced approach, combining theoretical insights with real-world applications, case studies, and technological frameworks. It covers essential topics such as AI-driven energy optimization, predictive maintenance, sustainable supply chain management, and generative AI’s role in product design and prototyping. By integrating perspectives from academia, industry leaders, and policymakers, this book provides a comprehensive understanding of AI’s role in sustainable manufacturing, making it an invaluable resource for researchers, engineers, industry practitioners, and decision-makers.
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The integration of artificial intelligence in manufacturing is transforming industries by optimizing production, reducing waste, and improving efficiency. However, as industries increasingly adopt AI-driven processes, concerns about sustainability and ethical AI deployment have emerged. This book explores the critical intersection of AI, sustainability, and generative AI technologies to advance responsible manufacturing practices. It provides an in-depth analysis of how AI can be leveraged to create eco-friendly, energy-efficient, and resource-conscious manufacturing systems while ensuring long-term viability and minimal environmental impact. The book adopts a balanced approach, combining theoretical insights with real-world applications, case studies, and technological frameworks. It covers essential topics such as AI-driven energy optimization, predictive maintenance, sustainable supply chain management, and generative AI’s role in product design and prototyping. By integrating perspectives from academia, industry leaders, and policymakers, this book provides a comprehensive understanding of AI’s role in sustainable manufacturing, making it an invaluable resource for researchers, engineers, industry practitioners, and decision-makers.
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Author / Editor Details
Anil B. Ghubade is an Assistant Professor in the School of Mechanical Engineering, Lovely Professional University, Punjab, India. He has published more than 15 research papers in national and international journals, two book chapters, and two patents. His areas of interest include manufacturing processes, machining, non-traditional machining, Bio-MEMS, and product design and development.
Anil B. Ghubade is an Assistant Professor in the School of Mechanical Engineering, Lovely Professional University, Punjab, India. He has published more than 15 research papers in national and international journals, two book chapters, and two patents. His areas of interest include manufacturing processes, machining, non-traditional machining, Bio-MEMS, and product design and development.
Chandan Deep Singh, PhD is an Assistant Professor in the Department of Mechanical Engineering, Punjabi University, Patiala. He has published more than 150 papers in peer-reviewed international journals and conferences, 59 reference books, four edited books, and more than 20 book chapters. His research focuses on computer-aided design, computer-aided manufacturing, and production and industrial engineering.
Jasvinder Singh, PhD is an Assistant Professor in the School of Mechanical Engineering at Lovely Professional University, Jalandhar. He has more than 35 publications in national and international journals and conferences of repute. His research interests include welding and fabrication, non-traditional machining processes, and general manufacturing.
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Table of Contents
Preface
1. Introduction to AI in Manufacturing
Mandeep Singh and Chandan Deep Singh
1.1 Introduction
1.2 Evolution of AI in Manufacturing and the Industry 4.0 Paradigm
1.2.1 Historical Context and Industry 4.0
1.2.2 Key Drivers of AI Adoption in Manufacturing
1.3 Big Data and Cloud Computing: Foundations for AI in Manufacturing
1.3.1 The Role of Big Data in Manufacturing
1.3.2 Cloud Computing as an Enabler of AI
1.4 AI Techniques and Applications in Smart Manufacturing
1.4.1 Overview of AI Methods in Manufacturing
1.4.2 Predictive Maintenance and Anomaly Detection
1.4.3 Autonomous Scheduling and Logistics
1.4.4 Access Control and Security via Blockchain
1.4.5 Unified Industrial Knowledge Models
1.5 Integration of AI with Enabling Technologies
1.5.1 Industrial Internet of Things (IIoT)
1.5.2 Robotics and Automation
1.5.3 Big Data Analytics and Decision Support
1.5.4 Blockchain and Secure Data Management
1.6 Challenges and Open Issues in AI-Driven Manufacturing
1.6.1 Data-Related Challenges
1.6.2 Model Interpretability and Explainability
1.6.3 Security, Privacy, and Adversarial Threats
1.6.4 Scalability and Integration
1.7 Future Research Directions and Opportunities
1.7.1 Toward Industry 5.0
1.7.2 Advanced AI Techniques and Architectures
1.7.3 Standardization, Regulation, and Best Practices
1.7.4 Case Studies and Benchmarking
1.8 Conclusion
References
2. Sustainable Challenges of Industry 5.0 Adoption in Manufacturing
Kumud Tiwari, Rekha Goyat and Mahipal Singh
2.1 Introduction
2.2 Application in Industry 5.0
2.3 Roadblocks to Industry 5.0 Adoption
2.3.1 Security
2.3.1.1 Authentication
2.3.1.2 Integrity
2.3.1.3 Access Control
2.3.1.4 Audit
2.3.2 Human-Machine Collaboration
2.3.3 Privacy
2.3.4 Scalability
2.3.5 Ethical Considerations and Regulatory Compliance
2.3.6 Skilled Workforce
2.4 Future Research Agenda of Sustainable AI in Industry 5.0
2.4.1 Lifecycle Assessment and Green AI Metrics
2.4.2 Adaptive and Context-Aware Algorithms
2.4.3 Human-AI Collaboration for Sustainability
2.4.4 Edge-Cloud Orchestration and Decentralized Intelligence
2.4.5 Ethical and Regulatory Frameworks
2.4.6 Circular Economy and Hardware Innovation
2.4.7 Cross-Disciplinary Collaboration
2.5 Result and Discussion
2.5.1 Literature Review Synthesis
2.5.2 Industry Practitioner Survey
2.5.3 Expert Interviews and Pilot Case Studies
2.5.4 Sustainable AI Strategy Effectiveness
2.6 Conclusion
References
3. Principles of Sustainable AI: Concepts, Challenges, and Governance
Chandra Tivrakrishna Panday, Ankur Srivastava and Vineet Pandey
3.1 Introduction
3.2 Key Principles of Sustainable AI
3.2.1 Energy Efficiency with Reduced Carbon Footprint
3.2.2 Life‑Cycle Thinking: From Dataset Generation to Model Deployment and Decommissioning
3.2.3 Data Efficiency and Minimization
3.2.4 Transparency, Explainability and Accountability
3.2.5 Human Centricity, Equity, and Social Justice
3.3 Challenges and Trade‑Offs
3.3.1 Equity vs. Resource Constraints
3.3.2 Transparency vs. Proprietary/Privacy Constraints
3.3.3 Regulatory and Policy Gaps
3.4 Future Directions and Open Research Questions
3.4.1 Techniques and Technologies Under Development
3.4.2 Interdisciplinary Research Needs
3.4.3 Social, Ethical, and Economic Implications Going Forward
3.5 Conclusion
3.6 Recommendations
3.7 Final Thoughts on Envisioning Sustainable AI
References
4. Synergistic Use of AI and Smart Materials in Energy Efficient Structural Design
Ankur Gupta, Lakshmi Narayanan V., T. Dhilip Kumar and Pratiksha Sarma
4.1 Introduction to the Growing Need for Energy Optimization
4.2 Overview of Smart Structures
4.3 Introduction to Smart Materials
4.3.1 Thermochromic Materials
4.3.2 Electrochromic Materials
4.3.3 Piezoelectric Materials
4.3.4 Shape-Memory Alloys (SMAs)
4.3.5 Ionic Polymer Metal Composite
4.3.6 Phase-Change Materials (PCMs)
4.4 The Role of AI in Smart Structures and Smart Material
4.5 AI-Driven Energy Generation and Distribution in Smart Structures
4.5.1 Introduction to AI in Energy Generation and Distribution
4.6 Optimizing Renewable Energy Generation
4.7 Smart Grids and AI in Energy Distribution
4.8 Energy Storage and AI Optimization
4.9 Dynamic Energy Distribution Systems within Smart Buildings
4.10 Conclusion
Acknowledgements
References
5. Predictive Maintenance and Sustainable Operations: A Paradigm Shift in Modern Industry
Mandeep Singh Rayat, Chandan Deep Singh, Jasvinder Singh and Mukhtiar Singh
5.1 Introduction
5.1.1 Environmental and Monetary Motives
5.1.2 Economic Imperatives
5.1.3 Environmental Imperatives
5.1.4 Important Manufacturing Uses
5.1.5 Energy Sector: Wind Turbine Predictive Digital Twins
5.1.6 Automotive and Heavy Industry
5.1.7 Smart Maintenance Platforms: The Siemens MindSphere Case
5.2 Literature Review
5.2.1 Predictive Maintenance in the Industry 4.0 Context
5.2.2 The Shift to Industry 5.0
5.3 Discussion
5.3.1 Multipillar Sustainability in Practice
5.3.2 Quantitative and Strategic Value
5.4 Conclusion
References
6. Integrating Artificial Intelligence in Sustainable Supply Chain Management: Concepts and Frameworks
Anil Ghubade, Basant Singh Sikarwar, Ankur Gupta, Adam Jacso and Rajeev Kumar Singh
6.1 Introduction
6.2 Supply Chain Management
6.3 Sustainability
6.4 Enhancing Supply Chain Sustainability through Industry 4.0
6.5 Smart Manufacturing and Supply Chain Sustainability
6.6 Blockchain Technology and Supply Chains
6.7 IoT in Sustainable Supply Chain Management
6.8 The AII Framework in SCM
6.9 Digital Twins in Sustainable Supply Chains
6.9.1 Usual Components of DT Disclosed for SCM Digital Twins
6.10 Real-World Effects and Interdisciplinary Coordination
for AI in SCM
6.11 Achieving Carbon Neutrality through Sustainable Supply
Chains
6.12 Conflicts, Challenges, and Unexpected Outcomes in Sustainable Supply Chain Strategies
6.13 Challenges to Sustainable Supply Chain in Using AI
6.14 Conclusion
References
7. Generative AI in Product Design and Prototyping
Minesh Vohra
7.1 Introduction
7.2 Deep Generative Models for Product Design
7.2.1 Core Model Families
7.2.2 Goal, Feasibility, and Diversity-Oriented Generation
7.2.3 Topology Optimization and Structural Design
7.3 Generative AI in Product Design and Prototyping Pipelines
7.3.1 CAD Ready Geometry from Image
7.3.2 Deep Generative Design for Mass Production
7.3.3 Rapid Prototyping and Additive Manufacturing
7.3.4 Certified Guidance and Safety‑Aware Generation
7.4 Human–AI Co‑Creation in Design and Prototyping
7.4.1 Getting the Right Design vs. Getting the Design Right
7.4.2 Interaction Design and UX Principles for GenAI Tools
7.4.3 Generative AI as Prototyping Material
7.4.4 Data-Driven Product Design Aspects
7.5 Opportunities and Challenges
7.5.1 Data, Representation, and Benchmarks
7.5.2 Manufacturability, Robustness, and Lifecycle Performance
7.5.3 Human Factors, Ethics, and Regulation
7.6 Conclusion
References
8. Additive Manufacturing and AI Optimization
Rehan Ahmed and Anil Ghubade
8.1 Introduction
8.2 Fundamentals of Additive Manufacturing
8.3 Artificial Intelligence in Additive Manufacturing
8.4 Optimization Techniques Using AI
8.5 Sustainability Considerations
8.6 Case Studies and Industrial Applications
8.6.1 Case Study 1: Boeing Streamlines Aerospace Production with AI-Driven Additive Manufacturing
8.6.2 Case Study 2: Advancements in Biomedical Implants through AI and Additive Manufacturing
8.6.3 Case Study 3: Czinger 21C — Revolutionizing Automotive Manufacturing with AI and 3D Printing
8.7 Challenges and Limitations
8.7.1 Data Quality and Model Generalization
8.7.2 Hardware-Software Integration Issues
8.7.3 High Initial Investment and Training Costs
8.7.4 Ethical Implications of AI in Design Decisions
8.8 Future Outlook and Research Directions
8.8.1 Industry 5.0 Ecosystem Integration
8.8.2 Research Gaps and Opportunities
8.9 Conclusion
References
9. Regulatory Frameworks: Rules, Ethics, and Compliance for Sustainable AI in Industry
Swastik Pradhan, Abhishek Barua and Manisha Priyadarshini
9.1 Introduction to AI Regulations in Manufacturing
9.1.1 Importance of Governance in AI-Enabled Industries
9.1.2 Sustainability as a Regulatory Priority
9.2 Global Regulatory Landscape
9.2.1 International Frameworks and Policy Anchors
9.2.1.1 EU AI Act (Regulatory Model Risk-Based and Binding)
9.2.1.2 OECD AI Principles (Soft-Law, Normative Baseline)
9.2.1.3 United Nations and SDGs (Policy Framing and Operational Incentives)
9.2.2 Role of Standards Bodies: ISO/IEC and IEEE
9.2.2.1 ISO/IEC (Management, Impact Assessment, Terminology)
9.2.2.2 IEEE Standards and P7000 Series (Ethical System Design)
9.2.3 Region and Implications for Manufacturers
9.2.4 Region Frameworks with Manufacturing Relevance
9.3 Ethical Principles for Sustainable AI
9.3.1 Bias and Discrimination Risks in AI-Driven Decision-Making
9.3.2 Human-Centered AI for Industry 5.0
9.4 Compliance in Sustainable Manufacturing
9.4.1 Data Privacy and Cybersecurity Regulations
9.4.2 Energy Consumption and Carbon Footprint Reporting
9.4.3 Lifecycle Assessments and Eco-Certifications for AI Tools
9.5 Generative AI-Specific Regulatory Challenges
9.5.1 Intellectual Property Rights and Patentability of AI-Generated Designs
9.5.2 Environmental Trade-Offs in Generative Design Optimization
9.5.3 Preventing Misuse and Ensuring Design Accountability
9.6 Future Outlook for a Harmonized Global Framework
9.6.1 Need for Cross-Border AI Governance in Manufacturing Supply Chains
9.6.2 Role of Multi-Stakeholder Collaborations
9.6.3 Emerging Trends in AI Regulation
9.6.4 Responsible, Sustainable AI Integration in Manufacturing
9.7 Conclusion
References
10. Future of Sustainable AI in Industry 5.0
Mahipal Singh and Rekha Goyat
10.1 Introduction
10.1.1 Contribution of the Study
10.2 Evolution of Industry from 1.0 to 5.0
10.3 Role of Sustainable AI in Industry 5.0
10.4 Role of AI-Maintained Sustainability in Industry 5.0
10.4.1 Energy Optimization and Management
10.4.2 Predictive Maintenance to Reduce Waste
10.4.3 Resource Efficiency through Process Optimization
10.4.4 Waste Reduction and Circular Economy Support
10.4.5 Supply Chain Sustainability
10.4.6 Green Product Design and Innovation
10.4.7 Low-Energy AI and Computational Sustainability
10.4.8 ESG Monitoring and Compliance
10.5 Roadmap for the Future of Sustainable AI in Industry 5.0
References
Index
Back to Top
Preface
1. Introduction to AI in Manufacturing
Mandeep Singh and Chandan Deep Singh
1.1 Introduction
1.2 Evolution of AI in Manufacturing and the Industry 4.0 Paradigm
1.2.1 Historical Context and Industry 4.0
1.2.2 Key Drivers of AI Adoption in Manufacturing
1.3 Big Data and Cloud Computing: Foundations for AI in Manufacturing
1.3.1 The Role of Big Data in Manufacturing
1.3.2 Cloud Computing as an Enabler of AI
1.4 AI Techniques and Applications in Smart Manufacturing
1.4.1 Overview of AI Methods in Manufacturing
1.4.2 Predictive Maintenance and Anomaly Detection
1.4.3 Autonomous Scheduling and Logistics
1.4.4 Access Control and Security via Blockchain
1.4.5 Unified Industrial Knowledge Models
1.5 Integration of AI with Enabling Technologies
1.5.1 Industrial Internet of Things (IIoT)
1.5.2 Robotics and Automation
1.5.3 Big Data Analytics and Decision Support
1.5.4 Blockchain and Secure Data Management
1.6 Challenges and Open Issues in AI-Driven Manufacturing
1.6.1 Data-Related Challenges
1.6.2 Model Interpretability and Explainability
1.6.3 Security, Privacy, and Adversarial Threats
1.6.4 Scalability and Integration
1.7 Future Research Directions and Opportunities
1.7.1 Toward Industry 5.0
1.7.2 Advanced AI Techniques and Architectures
1.7.3 Standardization, Regulation, and Best Practices
1.7.4 Case Studies and Benchmarking
1.8 Conclusion
References
2. Sustainable Challenges of Industry 5.0 Adoption in Manufacturing
Kumud Tiwari, Rekha Goyat and Mahipal Singh
2.1 Introduction
2.2 Application in Industry 5.0
2.3 Roadblocks to Industry 5.0 Adoption
2.3.1 Security
2.3.1.1 Authentication
2.3.1.2 Integrity
2.3.1.3 Access Control
2.3.1.4 Audit
2.3.2 Human-Machine Collaboration
2.3.3 Privacy
2.3.4 Scalability
2.3.5 Ethical Considerations and Regulatory Compliance
2.3.6 Skilled Workforce
2.4 Future Research Agenda of Sustainable AI in Industry 5.0
2.4.1 Lifecycle Assessment and Green AI Metrics
2.4.2 Adaptive and Context-Aware Algorithms
2.4.3 Human-AI Collaboration for Sustainability
2.4.4 Edge-Cloud Orchestration and Decentralized Intelligence
2.4.5 Ethical and Regulatory Frameworks
2.4.6 Circular Economy and Hardware Innovation
2.4.7 Cross-Disciplinary Collaboration
2.5 Result and Discussion
2.5.1 Literature Review Synthesis
2.5.2 Industry Practitioner Survey
2.5.3 Expert Interviews and Pilot Case Studies
2.5.4 Sustainable AI Strategy Effectiveness
2.6 Conclusion
References
3. Principles of Sustainable AI: Concepts, Challenges, and Governance
Chandra Tivrakrishna Panday, Ankur Srivastava and Vineet Pandey
3.1 Introduction
3.2 Key Principles of Sustainable AI
3.2.1 Energy Efficiency with Reduced Carbon Footprint
3.2.2 Life‑Cycle Thinking: From Dataset Generation to Model Deployment and Decommissioning
3.2.3 Data Efficiency and Minimization
3.2.4 Transparency, Explainability and Accountability
3.2.5 Human Centricity, Equity, and Social Justice
3.3 Challenges and Trade‑Offs
3.3.1 Equity vs. Resource Constraints
3.3.2 Transparency vs. Proprietary/Privacy Constraints
3.3.3 Regulatory and Policy Gaps
3.4 Future Directions and Open Research Questions
3.4.1 Techniques and Technologies Under Development
3.4.2 Interdisciplinary Research Needs
3.4.3 Social, Ethical, and Economic Implications Going Forward
3.5 Conclusion
3.6 Recommendations
3.7 Final Thoughts on Envisioning Sustainable AI
References
4. Synergistic Use of AI and Smart Materials in Energy Efficient Structural Design
Ankur Gupta, Lakshmi Narayanan V., T. Dhilip Kumar and Pratiksha Sarma
4.1 Introduction to the Growing Need for Energy Optimization
4.2 Overview of Smart Structures
4.3 Introduction to Smart Materials
4.3.1 Thermochromic Materials
4.3.2 Electrochromic Materials
4.3.3 Piezoelectric Materials
4.3.4 Shape-Memory Alloys (SMAs)
4.3.5 Ionic Polymer Metal Composite
4.3.6 Phase-Change Materials (PCMs)
4.4 The Role of AI in Smart Structures and Smart Material
4.5 AI-Driven Energy Generation and Distribution in Smart Structures
4.5.1 Introduction to AI in Energy Generation and Distribution
4.6 Optimizing Renewable Energy Generation
4.7 Smart Grids and AI in Energy Distribution
4.8 Energy Storage and AI Optimization
4.9 Dynamic Energy Distribution Systems within Smart Buildings
4.10 Conclusion
Acknowledgements
References
5. Predictive Maintenance and Sustainable Operations: A Paradigm Shift in Modern Industry
Mandeep Singh Rayat, Chandan Deep Singh, Jasvinder Singh and Mukhtiar Singh
5.1 Introduction
5.1.1 Environmental and Monetary Motives
5.1.2 Economic Imperatives
5.1.3 Environmental Imperatives
5.1.4 Important Manufacturing Uses
5.1.5 Energy Sector: Wind Turbine Predictive Digital Twins
5.1.6 Automotive and Heavy Industry
5.1.7 Smart Maintenance Platforms: The Siemens MindSphere Case
5.2 Literature Review
5.2.1 Predictive Maintenance in the Industry 4.0 Context
5.2.2 The Shift to Industry 5.0
5.3 Discussion
5.3.1 Multipillar Sustainability in Practice
5.3.2 Quantitative and Strategic Value
5.4 Conclusion
References
6. Integrating Artificial Intelligence in Sustainable Supply Chain Management: Concepts and Frameworks
Anil Ghubade, Basant Singh Sikarwar, Ankur Gupta, Adam Jacso and Rajeev Kumar Singh
6.1 Introduction
6.2 Supply Chain Management
6.3 Sustainability
6.4 Enhancing Supply Chain Sustainability through Industry 4.0
6.5 Smart Manufacturing and Supply Chain Sustainability
6.6 Blockchain Technology and Supply Chains
6.7 IoT in Sustainable Supply Chain Management
6.8 The AII Framework in SCM
6.9 Digital Twins in Sustainable Supply Chains
6.9.1 Usual Components of DT Disclosed for SCM Digital Twins
6.10 Real-World Effects and Interdisciplinary Coordination
for AI in SCM
6.11 Achieving Carbon Neutrality through Sustainable Supply
Chains
6.12 Conflicts, Challenges, and Unexpected Outcomes in Sustainable Supply Chain Strategies
6.13 Challenges to Sustainable Supply Chain in Using AI
6.14 Conclusion
References
7. Generative AI in Product Design and Prototyping
Minesh Vohra
7.1 Introduction
7.2 Deep Generative Models for Product Design
7.2.1 Core Model Families
7.2.2 Goal, Feasibility, and Diversity-Oriented Generation
7.2.3 Topology Optimization and Structural Design
7.3 Generative AI in Product Design and Prototyping Pipelines
7.3.1 CAD Ready Geometry from Image
7.3.2 Deep Generative Design for Mass Production
7.3.3 Rapid Prototyping and Additive Manufacturing
7.3.4 Certified Guidance and Safety‑Aware Generation
7.4 Human–AI Co‑Creation in Design and Prototyping
7.4.1 Getting the Right Design vs. Getting the Design Right
7.4.2 Interaction Design and UX Principles for GenAI Tools
7.4.3 Generative AI as Prototyping Material
7.4.4 Data-Driven Product Design Aspects
7.5 Opportunities and Challenges
7.5.1 Data, Representation, and Benchmarks
7.5.2 Manufacturability, Robustness, and Lifecycle Performance
7.5.3 Human Factors, Ethics, and Regulation
7.6 Conclusion
References
8. Additive Manufacturing and AI Optimization
Rehan Ahmed and Anil Ghubade
8.1 Introduction
8.2 Fundamentals of Additive Manufacturing
8.3 Artificial Intelligence in Additive Manufacturing
8.4 Optimization Techniques Using AI
8.5 Sustainability Considerations
8.6 Case Studies and Industrial Applications
8.6.1 Case Study 1: Boeing Streamlines Aerospace Production with AI-Driven Additive Manufacturing
8.6.2 Case Study 2: Advancements in Biomedical Implants through AI and Additive Manufacturing
8.6.3 Case Study 3: Czinger 21C — Revolutionizing Automotive Manufacturing with AI and 3D Printing
8.7 Challenges and Limitations
8.7.1 Data Quality and Model Generalization
8.7.2 Hardware-Software Integration Issues
8.7.3 High Initial Investment and Training Costs
8.7.4 Ethical Implications of AI in Design Decisions
8.8 Future Outlook and Research Directions
8.8.1 Industry 5.0 Ecosystem Integration
8.8.2 Research Gaps and Opportunities
8.9 Conclusion
References
9. Regulatory Frameworks: Rules, Ethics, and Compliance for Sustainable AI in Industry
Swastik Pradhan, Abhishek Barua and Manisha Priyadarshini
9.1 Introduction to AI Regulations in Manufacturing
9.1.1 Importance of Governance in AI-Enabled Industries
9.1.2 Sustainability as a Regulatory Priority
9.2 Global Regulatory Landscape
9.2.1 International Frameworks and Policy Anchors
9.2.1.1 EU AI Act (Regulatory Model Risk-Based and Binding)
9.2.1.2 OECD AI Principles (Soft-Law, Normative Baseline)
9.2.1.3 United Nations and SDGs (Policy Framing and Operational Incentives)
9.2.2 Role of Standards Bodies: ISO/IEC and IEEE
9.2.2.1 ISO/IEC (Management, Impact Assessment, Terminology)
9.2.2.2 IEEE Standards and P7000 Series (Ethical System Design)
9.2.3 Region and Implications for Manufacturers
9.2.4 Region Frameworks with Manufacturing Relevance
9.3 Ethical Principles for Sustainable AI
9.3.1 Bias and Discrimination Risks in AI-Driven Decision-Making
9.3.2 Human-Centered AI for Industry 5.0
9.4 Compliance in Sustainable Manufacturing
9.4.1 Data Privacy and Cybersecurity Regulations
9.4.2 Energy Consumption and Carbon Footprint Reporting
9.4.3 Lifecycle Assessments and Eco-Certifications for AI Tools
9.5 Generative AI-Specific Regulatory Challenges
9.5.1 Intellectual Property Rights and Patentability of AI-Generated Designs
9.5.2 Environmental Trade-Offs in Generative Design Optimization
9.5.3 Preventing Misuse and Ensuring Design Accountability
9.6 Future Outlook for a Harmonized Global Framework
9.6.1 Need for Cross-Border AI Governance in Manufacturing Supply Chains
9.6.2 Role of Multi-Stakeholder Collaborations
9.6.3 Emerging Trends in AI Regulation
9.6.4 Responsible, Sustainable AI Integration in Manufacturing
9.7 Conclusion
References
10. Future of Sustainable AI in Industry 5.0
Mahipal Singh and Rekha Goyat
10.1 Introduction
10.1.1 Contribution of the Study
10.2 Evolution of Industry from 1.0 to 5.0
10.3 Role of Sustainable AI in Industry 5.0
10.4 Role of AI-Maintained Sustainability in Industry 5.0
10.4.1 Energy Optimization and Management
10.4.2 Predictive Maintenance to Reduce Waste
10.4.3 Resource Efficiency through Process Optimization
10.4.4 Waste Reduction and Circular Economy Support
10.4.5 Supply Chain Sustainability
10.4.6 Green Product Design and Innovation
10.4.7 Low-Energy AI and Computational Sustainability
10.4.8 ESG Monitoring and Compliance
10.5 Roadmap for the Future of Sustainable AI in Industry 5.0
References
Index
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