Futuristic Trends in Intelligent Manufacturing: Optimization and Intelligence in Manufacturing (Materials Forming, Machining and Tribology)

This book shows how Industry 4.0 is a strategic approach for integrating advanced control systems with Internet technology enabling communication between people, products and complex systems. It includes processes such as machining features, machining knowledge, execution control, operation planning, machine tool selection and cutting tool. This book focuses on different articles related to advanced technologies, and their integration to foster Industry 4.0, being useful for researchers as well as industrialists to refer and utilize the information in production control. Preface Contents About the Editors Smart Manufacturing—A Lead Way to Sustainable Manufacturing 1 Introduction 2 Smart Manufacturing and Requirements for Implementation 2.1 Smart Machining 3 Conclusions References Smart Machining of Titanium Alloy Using ANN Encompassed Prediction Model and GA Optimization 1 Introduction 2 Experimental Design 3 Methodology 3.1 Response Surface Model 3.2 Genetic Algorithm (GA) 3.3 ANN Prediction Model 3.4 Results and Discussions 4 Conclusions References Fuzzy Interference System of Drilling Parameters for Delrin Parts 1 Introduction 2 Material and Experimental Data 3 Fuzzy Interference System (FIS) 4 Conclusions References Optimization and Effect Analysis of Sustainable Micro Electrochemical Machining Using Organic Electrolyte 1 Introduction 2 Materials and Experimental Details 2.1 Experimental Device for EMM Process 2.2 Input Factors Used for the Experiments 3 Optimization by GRA 3.1 Different Stages of GRA 3.2 GRA Results 4 Discussion 4.1 Optimal Parametric Combination 4.2 How the Input Factors Affect the EMM Process? 4.3 Micro-hole Images 5 Conclusions References Artificial Fish Swarm Algorithm Driven Optimization for Copper-Nano Particles Suspended Sodium Nitrate Electrolyte Enabled ECM on Die Tool Steel 1 Introduction 2 Copper Metal Nano-particles Analysis 3 Experimentation 3.1 Machining Setup 3.2 Characteristics of the Selected Workpiece Material 3.3 Design of Experiments 4 Optimization Using Artificial Fish-Swarm Algorithm 5 Results and Discussion 6 Validation Test 7 Conclusion References Comparative Analysis Between Conventional Method Versus Machine Learning Method for Pipeline Condition Prediction 1 Introduction 1.1 Overview of the Literature Review 1.2 Scope of This Comparative Analysis and Contributions 2 Motivation for Using Machine Learning for Pipeline Prediction 2.1 Existing Pipeline Maintenance Program and Its Challenges 2.2 Machine Learning Based Solution for Pipeline Prediction 3 Cases for Pipeline Prediction 3.1 Pipeline Problem Areas 3.2 Methods Used for Pipeline Prediction 4 Pipeline Prediction Using Conventional Method 4.1 Summary of Survey for Pipeline Prediction Using Conventional Methods 4.2 Accuracy of Prediction and Challenges on Conventional Method 5 Machine Learning (ML) Based Solution for Pipeline Prediction 5.1 Type of ML Prediction (Classification/Regression) 5.2 Comparison Between Machine Learning and Conventional Methods 5.3 Comparison of Machine Learning Methods in Pipeline Prediction 5.4 Accuracy of Machine Learning Algorithm Methods 6 Future Research Challenges 6.1 Availability of Data 6.2 Modification of Infrastructure for Machine Learning and Complexity 6.3 Organizational Capability in Handling Machine Learning 7 Conclusion References Application of Back Propagation Algorithm in Optimization of Weave Friction Stir Welding—A Study 1 Introduction 2 Experimental Work 2.1 Selection of the Range of Process Parameters 3 Optimization of Process Parameters 4 Results and Discussion 4.1 Consequence of Tool Rotational Speed Without GNPs 4.2 The Effect of Tool Rotational Speed with GNPs 4.3 The Effect of Welding Speed Without GNPs 4.4 The Effect of Welding Speed with GNPs 4.5 The Effect of Axial Load Without GNPs 4.6 The Effect of Axial Load with GNPs 5 Conclusion References ANFIS and RSM Modelling Analysis on Surface Roughness of PB Composites in Drilling with HSS Drills 1 Introduction 2 Materials and Measurements 2.1 Plan of Experiments 2.2 Measurement of Surface Roughness 3 Method of Analysis 3.1 Response Surface Methodology (RSM) 3.2 Adaptive Neuro Fuzzy Inference System (ANFIS) 4 Results and Discussion 4.1 RSM Analysis 4.2 Adaptive-Neuro Fuzzy Inference System (ANFIS) Analysis 4.3 ANOVA Analysis 4.4 Control Factors and Their Interaction Effects 4.5 Comparison of RSM and ANFIS Models 4.6 Confirmation Experiments 5 Conclusions References Machine Learning for Smart Manufacturing for Healthcare Applications 1 Introduction 2 Machine Learning Algorithms 2.1 Supervised Learning Algorithms 2.2 Unsupervised Learning Algorithms 2.3 Reinforcement Learning 3 Smart Manufacturing 4 Applications of Machine Learning in Healthcare 4.1 Disease Diagnosis and Prediction 4.2 Clinical Trials and Drug Discovery 4.3 Outbreak Prediction 4.4 Rehabilitation Equipment 5 Benefits, Opportunities, Challenges, and Future Direction 6 Conclusion References A Comparative Analysis of Two Soft Computing Methods for Sales Forecasting in Dairy Production: A Case Study 1 Introduction 2 Gene Expression Programming (GEP) 3 Artificial Neural Network 4 Case Study and Data Description 5 Prediction by GEP 6 Prediction by ANN 7 Result and Discussion 8 Conclusions References AR and VR in Manufacturing 1 Introduction 2 Virtual Reality in Manufacturing Sector 3 Augmented Reality in Manufacturing Sector 3.1 Types of Virtual Reality and Augmented Reality 3.2 Top Markerless Augmented Reality SDKs 4 Implementing AR and VR in Manufacturing 4.1 Applications for AR in Manufacturing 4.2 Reducing Mental Workload and Cognitive Load 4.3 AR and VR Use Cases 4.4 Challenges Faced by AR and VR 5 Conclusion References Industrial IoT and Intelligent Manufacturing 1 Introduction 2 Methods 3 Discussion 3.1 Predictive Maintenance 3.2 Remote Control and Monitoring in Production 3.3 Asset Tracking 3.4 Logistics and Supply Chain Management 3.5 Digital Twin Technology 4 Conclusion References Cyber-Physical Systems: A Pilot Adoption in Manufacturing 1 Introduction 1.1 The 4th Industrial Revolution (IR4) 1.2 Business Needs and Economies Drive Adoption 1.3 Cyber-Physical Systems as a Key Technology for Digitization in Industry 4.0 1.4 Retrofitting as a Solution for Digitization of SMEs 2 Cyber Physical Systems as a Design Concept 2.1 Overview 2.2 Viewpoints and Definitions of CPS 2.3 Existing CPS Architectures 2.4 CPS Meta-Models 3 Retrofitting Cyber-Physical Systems in Fabric Manufacturing: A Case Study 3.1 Methodology 3.2 System Overview 3.3 Results and Outcomes 4 Challenges and Future Trends of CPS in Manufacturing Automation 5 Conclusion References Intelligent Machining of Abrasive Jet on Carbon Fiber and Glass Fiber Polymeric Composites Using Modified Nozzle 1 Introduction 1.1 Polymer Composites 1.2 Abrasive Jet Machining of Polymer Composite 2 Need for Modified Nozzle 2.1 Modified Nozzle 3 Materials and Methods 3.1 Ascertaining the Essential Process Parameters 3.2 Experimental Procedure 4 Results and Discussion 4.1 Effect of Process Parameters on CFRP Composites 4.2 Effect of Process Parameters in GFRP 5 Conclusions References Additive Manufacturing of Nylon Parts and Implication Study on Change in Infill Densities and Structures 1 Introduction 2 Materials and Methods 2.1 Material 2.2 Model and Fabrication 2.3 Experimental Study 3 Results and Discussion 3.1 Influence of Infill Parameters on Tensile Strength 3.2 Influence of Infill Parameters on Compression Strength 3.3 Influence of Infill Parameters on Flexural Strength 3.4 Economic Implication 4 Conclusion References Index