Manufacturing in the Digital Age

Preface, Acknowledgment, Editorâs Bibliography, List of Contributors, Foreword, Chapter 1 Introduction to Manufacturing in the Digital Age 1.1 The Order from First Production to the Industrial Revolution 1.1.1 Definition and History of Manufacturing 1.1.2 Traditional Manufacturing Processes 1.1.3 Challenges and Limitations of Traditional Manufacturing 1.2 Revolutionizing Manufacturing with Digital Transformation 1.2.1 Definition of Digital Manufacturing 1.2.2 Key Factors of Digital Transformation in Manufacturing 1.2.3 The Role of Industry 4.0 1.3 Key Technologies in Digital Manufacturing 1.3.1 IoT 1.3.2 Big Data and Analytics 1.3.3 AI and ML 1.3.4 Robotics and Automation 1.3.5 Additive Manufacturing (3D Printing) 1.3.6 Digital Twins 1.3.7 Cloud Computing 1.3.8 Cybersecurity 1.4 Impact of Digital Manufacturing 1.4.1 On Product Development 1.4.2 On Supply Chain Management 1.4.3 On Workforce 1.5 Case Studies and Real-World Examples 1.5.1 Success Stories from Leading Manufacturers 1.5.2 Challenges Faced and Overcome 1.5.3 Lessons Drawn and Leading Practices 1.6 Benefits of Digital Manufacturing 1.6.1 Increased Efficiency and Productivity 1.6.2 Cost Reduction 1.6.3 Improved Quality and Consistency 1.6.4 Enhanced Flexibility and Agility 1.7 Challenges and Barriers to Adoption 1.7.1 Technical Challenges 1.7.2 Cost and Investment Considerations 1.7.3 Workforce Resistance and Cultural Change 1.7.4 Data Privacy and Security Concerns 1.8 Future Trends in Digital Manufacturing 1.8.1 Emerging Technologies 1.8.2 Predictions for the Next Decade 1.8.3 The Role of Sustainability and Green Manufacturing 1.9 Conclusion Chapter 2 Advancements in Industry 4.0 and Introduction to Industry 5.0 2.1 Introduction 2.1.1 Overview of the Industrial Revolutions 2.1.2 Introduction to I4o and I5o 2.1.3 Chapter Objectives 2.2 Core Technologies of I4o 2.2.1 IoT 2.2.2 AI and ML 2.2.3 CPS 2.2.4 Big Data Analytics 2.2.5 Robotics and Automation 2.3 Applications and Implications of I4o 2.3.1 Smart Factories 2.3.2 SCM 2.3.3 Predictive Maintenance and AI in Manufacturing 2.3.4 Digital Twins 2.4 I5o: A Human-Centric Approach 2.4.1 From Automation to Augmentation 2.4.2 Ethical and Value-Oriented Technology in I5o 2.4.3 Resilience and Sustainability 2.4.4 Customization and Personalization 2.5 Key Technologies Enabling I5o 2.5.1 Advanced AI and Robotics 2.5.2 CPS in I5o 2.5.3 Metaverse and Virtual Collaboration Tools 2.6 Transitioning from I4o to I5o 2.6.1 Evolutionary Path 2.6.2 Overcoming Challenges in the Transition 2.6.3 Collaborative Ecosystem 2.7 Case Studies and Practical Insights 2.7.1 Smart Factories in Action 2.7.2 Personalized Manufacturing 2.8 Conclusion 2.8.1 Summary of Key Innovations Driving I4o and I5o 2.8.2 Outlook on Future Developments in I5o Chapter 3 Product Design and Development in the Digital Era 3.1 Introduction 3.2 Digital Design and Prototyping 3.2.1 Computer-Aided Design (CAD) 3.2.2 Virtual Reality (VR) and Augmented Reality (AR) 3.3 Collaborative Design and Development 3.3.1 Cloud-Based Collaboration 3.3.2 Integrated Design and Development Tools 3.3.3 Communication and Messaging Platforms 3.3.4 Project Management and Collaboration Tools 3.3.5 User Testing and Feedback 3.3.6 Security and Access Controls 3.4 Data-Driven Decision-Making 3.4.1 User Behavior Analysis 3.4.2 Performance Analytics 3.4.3 Iterative Design and Development 3.4.4 Predictive Analytics 3.4.5 Cost-Benefit Analysis 3.4.6 Security Analytics 3.5 Sustainability and Efficiency 3.5.1 Green Software Engineering 3.5.2 Renewable Energy in Data Centers 3.5.3 Life Cycle Assessment (LCA) for Digital Products 3.5.4 Agile and DevOps for Efficiency 3.5.5 Automation and Efficiency 3.5.6 User Experience (UX) and Performance Optimization 3.5.7 Remote Collaboration and Sustainable Work Practices 3.5.8 Resource Efficiency in Cloud Computing 3.6 Challenges 3.6.1 Data Privacy and Security 3.6.2 Rapid Technological Changes 3.6.3 User Experience (UX) Design Challenges 3.6.4 Artificial Intelligence (AI) Integration Challenges 3.6.5 Positive and Negative Social Effects 3.6.6 Skill Gaps 3.6.7 Reluctance to Change 3.7 Case Studies 3.7.1 Digital Transformation at Siemens 3.7.2 Nikeâs Digital Innovation 3.7.3 Fordâs Electrification Strategy 3.7.4 Adobeâs Creative Cloud 3.7.5 Samsungâs Product Innovation 3.7.6 Nike+ 3.7.7 Starbucksâ Mobile App 3.7.8 Peloton Interactive Fitness 3.7.9 HelloFresh Meal Kits 3.7.10 Netflix 3.8 Future Trends 3.8.1 Human-Centered and Inclusive Design 3.8.2 Customization and Personalization 3.8.3 Digital Twins 3.8.4 IoT Integration 3.8.5 AR and VR in Product Prototyping 3.8.6 Robotics and Automation 3.8.7 Biometric Integration 3.8.8 Edge Computing in Product Functionality 3.8.9 Voice User Interfaces (VUI) 3.8.10 Blockchain for Supply Chain Transparency 3.8.11 Emphasis on Emotional Design 3.9 Conclusions Chapter 4 Internet of Things (IoT) in Smart Manufacturing 4.1 Introduction 4.1.1 Manufacturing 4.1.2 IoT 4.2 Application of IOT 4.2.1 Enhanced Operational Efficiency 4.2.2 Predictive Maintenance 4.2.3 Quality Improvement 4.2.4 Supply Chain Optimization 4.2.5 Energy Efficiency 4.2.6 Customization and Flexibility 4.2.7 Innovation and Competitive Advantage 4.2.8 Data Integration and Analytics 4.3 Digitalization of Manufacturing Processes Relies Heavily on Internet of Things (IoT) 4.4 To Enable Real-Time Data Collection 4.5 Predictive Maintenance 4.6 Improved Supply Chain Management 4.7 Industry 4.0 and Industry 5.0 are Integrating IoT 4.8 Sensor 4.9 Devices 4.10 Networks 4.11 Conclusion Chapter 5 Sustainable Manufacturing Practices in the Digital Age 5.1 Introduction 5.1.1 Sustainable Manufacturing in a Digital Context 5.2 Function of Additive Manufacturing in the Digital Era of Sustainable Manufacturing 5.3 Challenges to Implementing Sustainable Manufacturing Practices in the Digital Age 5.4 Innovation and Technology for Sustainability 5.5 New Developments in Digital Sustainability Trends 5.5.1 Green AI. 5.5.2 Bio-Manufacturing 5.5.3 Edge Computing 5.5.4 Decentralized Energy Grids 5.6 The Digital Transformation of Sustainability 5.6.1 Smart Factories: A Step Toward Zero Waste 5.7 The Benefits of Sustainable Manufacturing Practices in Digital Ages 5.8 Smart Manufacturing: The Path to Sustainability 5.9 The Future of Sustainable Manufacturing in the Digital Age 5.10 Conclusion Chapter 6 Challenges and Solutions of Industry 4.0 to Industry 5.0 6.1 Introduction to Industry 4.0 6.2 Challenges and Solutions in Adopting Industry 4.0 6.3 Datasets 6.4 Challenges and Solutions in Adopting Industry 5.0 6.5 Conclusions Chapter 7 Sustainable Industrial Transformation: Exploring Industry 4.0 and 5.0 7.1 Introduction 7.2 Evolution of Industrial Revolutions 7.3 Challenges of Industry 4.0 7.4 A Strategic Method to Meet the Obstacles 7.5 Industry 5.0: A Strategic Solution 7.6 Technological Advancements in Industry 4.0 and 5.0 7.7 Future Research Directions 7.8 Conclusion Chapter 8 Artificial Neural Networks with Training Algorithms for Prediction of Surface Roughness in Electrical Discharge Machining 8.1 Introduction 8.1.1 Research Novelty 8.2 Experiments and Methods 8.2.1 Experimental Set Up 8.2.2 Design of Experiment 8.2.3 Surface Roughness 8.2.4 Artificial Neural Networks 8.2.5 ANN Training Algorithms 8.2.6 Levenbergâ"Marquardt Algorithm 8.2.7 Conjugate Gradient Algorithm 8.2.8 Resilient-Backpropagation Algorithm 8.2.9 Evaluation Criteria 8.3 Results and Discussion 8.4 Conclusions Chapter 9 Effect of Performance Measures on Lean Manufacturing System Considering Industry 4.0 9.1 Introduction 9.2 Lean Manufacturing System: An Overview 9.2.1 Machine-Level Configurations 9.2.2 System Base Configurations 9.3 Process 9.4 Changeover Time 9.5 Conclusion and Future Scope Chapter 10 Humanâ"Machine Harmony in the Production Sector: How Automation is Transforming Manufacturing 10.1 Introduction 10.2 Fixed Automation 10.2.1 Definition and Characteristics of Fixed Automation 10.2.2 Examples of Fixed Automation 10.2.3 Advantages of Fixed Automation 10.2.4 Limitations of Fixed Automation 10.3 Fluid Automation 10.3.1 Concept and Definition 10.3.2 Key Components of Fluid Automation 10.3.3 Applications of Fluid Automation 10.3.4 Advantages and Challenges of Fluid Automation 10.4 Humanâ"Machine Collaboration 10.4.1 Role of Automation in Repetitive and Advanced Tasks 10.4.2 Workforce Skill Requirements 10.4.3 Designing Workplaces for Safety and Collaboration 10.5 Industry 4.0 Integration 10.5.1 Industry 4.0 Vision and Goals 10.5.2 Importance of Interoperability 10.5.3 Real-Time Information Sharing and Adaptability 10.6 Conclusion 10.6.1 Synergy between Humans and Machines 10.6.2 Fluid Automation as a Path to Flexibility 10.6.3 Future Manufacturing Outlook Chapter 11 Efficient Automation Solution for Invoice Data Extraction and Compilation: A Streamlined Approach for Enhanced Productivity 11.1 Introduction 11.2 Introduction to Robotic Process Automation (RPA) and its Advantages 11.3 Purpose and Significance of the Study 11.4 Methodology 11.4.1 Overview of Selected RPA Tools (e.g., UiPath, Automation Anywhere) 11.4.2 Step-By-Step Explanation of the Automation Process 11.4.3 Handling Failed Extractions 11.5 Implementation Steps 11.6 Results and Discussion 11.6.1 Performance Metrics (Accuracy, Time Savings) 11.6.2 Sample Outputs (e.g., Processed Invoices, Excel Data) 11.6.3 Comparative Analysis with Manual Processing Methods 11.7 Benefits of Automation 11.8 Challenges and Limitations of Automation Systems 11.9 Challenges in Manufacturing Environments 11.10 Solutions Tailored to Manufacturing 11.11 Conclusion Chapter 12 Modernizing and Streamlining Vendor Records Using Robotics Process Automation for Uplifting Business 12.1 Introduction 12.1.1 Basics of Robotics Process Automation (RPA) 12.1.2 How it Works 12.1.3 Importance of RPA 12.2 Literature Review 12.2.1 Presence of RPA 12.2.2 History 12.2.3 Features and Main Characteristics 12.2.4 Types of RPA 12.2.5 Artificial Intelligence and RPA 12.3 Use Case Example 12.3.1 Current Process Followed in the Chudekar Agro Pvt. Ltd. Company 12.3.2 Future Changes Can be Done Using RPA 12.4 Advantages of RPA 12.5 Disadvantages Rather Challenges of RPA 12.6 Current RPA Providers 12.7 The RPA Marketâs Future is Fueled by Hyper Automation 12.8 Conclusion References. Index.