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Soft Computing in Advanced Manufacturing Processes

Edited by Sandip Kunar, Elumalai P.V., Sridevi Gamini, S. Rama Sree and M. Sreenivasa Reddy
Series: Advances in Production Engineering
Copyright: 2026   |   Expected Pub Date:2026/08/30
ISBN: 9781394470150  |  Hardcover  |  
402 pages

One Line Description
Transform your manufacturing operations for the Industry 4.0 era with this essential guide, which delivers the practical AI and soft computing strategies you need to master complexity, optimize efficiency, and build resilient, smart production systems.

Audience
Mechanical, industrial, and manufacturing engineers, materials scientists, and automation specialists across both academia and industry working in areas such as smart manufacturing, artificial intelligence in production, and Industry 4.0 technologies.

Description
The advancement of manufacturing technologies has consistently been linked to progress in modern computational methods. In recent years, the integration of soft computing techniques has emerged as an essential factor in improving the adaptability, intelligence, and efficiency of manufacturing processes. Unlike traditional deterministic approaches, soft computing methods are capable of managing complexity, uncertainty, and imprecision that are integral in current manufacturing systems, enabling more effective optimization, decision-making, and process control. This book provides an in-depth exploration of how soft computing techniques are transforming the landscape of modern manufacturing. Integrating intelligent computation with engineering principles, the book demonstrates how these methods enhance decision-making, optimize complex processes, and enable adaptive control across diverse manufacturing operations. Covering applications in machining, process planning, scheduling, quality assurance, and fault diagnosis, it bridges the gap between traditional manufacturing and the new era of Industry 4.0. It emphasizes the role of artificial intelligence, machine learning, and data-driven technologies in achieving precision, flexibility, and sustainability. Through detailed case studies, comparative analyses, and practical insights, the book provides readers with both theoretical foundations and implementation strategies. Ideal for engineers, researchers, and professionals in academia and industry, this book serves as a comprehensive guide to leveraging soft computing for smarter, more efficient, and resilient manufacturing systems, paving the way toward a future of intelligent and sustainable production.
Readers will find the volume:
• Integrates cutting-edge soft computing methods such as fuzzy logic, neural networks, and genetic algorithms for intelligent manufacturing solutions;
• Demonstrates real-world applications in process optimization, quality control, and predictive maintenance;
• Bridges the gap between traditional manufacturing and Industry 4.0 through adaptive and data-driven approaches.

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Author / Editor Details
Sandip Kunar, PhD is an Assistant Professor in the Department of Mechanical Engineering at the Aditya Engineering College, Andhra Pradesh, India. He has published more than 40 research papers in various reputed international journals, more than 25 research papers in national and international conference proceedings, 70 book chapters, 24 books, and two patents.

Elumalai P.V., PhD is a Professor in the Department of Mechanical Engineering at Aditya University, Andhra Pradesh, India with more than 12 years of experience. He has published more than 135 research papers in peer-reviewed journals and several book chapters.

Sridevi Gamini, PhD is an Associate Professor in the Department of Electronics and Communication Engineering at Aditya University, Andhra Pradesh, India. She has published 13 research papers in national and international journals and 10 research papers in reputed international conference proceedings.

S. Rama Sree, PhD is a Professor in the Computer Science and Engineering Department and Pro Vice-Chancellor of Academics at Aditya University, Andhra Pradesh, India. She has published 35 papers in international journals, 15 papers in national and international conferences, and four patents, and has co-authored one book.

M. Sreenivasa Reddy, PhD is the Director of the Aditya Group of Educational Institutions and the Deputy Pro Chancellor of Aditya University, Andhra Pradesh, India. He has published more than 50 research papers in various reputed international journals, national and international conference proceedings, 20 book chapters, three books, and eleven patents.

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Table of Contents
Preface
Acknowledgement
1. Introduction to Smart Soft Computing in AdvancedManufacturing Processes
Sarthak Prasad Sahoo
1.1 Introduction
1.2 Motivation for Integrating Smart Technologies
1.3 Soft Computing and Its Role in Modern Manufacturing
1.4 An Overview of Advanced Manufacturing Processes (AMPs)
1.5 Smart Soft Computing in Manufacturing
1.6 Challenges and Limitations
1.7 Conclusion
References
2. Soft Computing Techniques in Smart Manufacturing
Sandip Kunar, Jagadeesha T., Anusha Mylavarapu, Ajithkumar Sitharaj, Gurudas Mandal and Preeti Singh Bahadur
2.1 Introduction
2.2 Key Technologies
2.2.1 IoT, AI, and ML’s Function in Material Design
2.2.1.1 IoT in Material Design
2.2.1.2 AI in Manufacturing and Material Design
2.2.1.3 ML in Manufacturing
2.2.1.4 Smart Manufacturing Using Integrated Methods
2.2.2 Advanced Processing Methods and AM
2.2.2.1 Laser Engineered Net Shaping (LENS)
2.2.2.2 Selective Laser Sintering (SLS)
2.2.2.3 Continuous Liquid Interface Production (CLIP) and Stereolithography (SLA)
2.2.3 Integrating Cyber-Physical Systems with Digital Twins
2.2.4 Real-Time Monitoring and Adaptive Control Systems
2.2.4.1 AI-Driven Adaptive Control in Manufacturing
2.2.4.2 Adaptive Control in Advanced Manufacturing
2.3 Intelligent Production Methods for Superior Materials
2.3.1 Development and Optimization of Advanced Alloys
2.3.2 Nanostructured and Composite Material Production
2.3.3 Tailoring Microstructures for Enhanced Properties
2.3.4 Technologies for Surface Engineering and Coatings
2.4 Difficulties with Intelligent Production of High-Performance Materials
2.4.1 Technical Difficulties in Integration and Process Control
2.4.2 Transitioning from Lab to Industrial Manufacturing
2.4.3 Economic Factors: Price and Effectiveness
2.4.4 Ensuring Manufacturing Consistency and Quality
2.5 Case Studies and Applications
2.5.1 Effective Applications in the Energy, Automotive, and Aerospace Sectors
2.5.2 Effect on Lifecycle Management and Material Performance
2.6 Opportunities, Challenges, and Future Directions
2.6.1 Development of Manufacturing Technologies and Smart Materials
2.6.2 Predictive Material Design Using AI and Machine Learning
2.6.3 Sustainable Manufacturing Practices and Resource Efficiency
2.6.4 Policy, Regulations, and Industry Collaboration
2.6.5 Difficulties and Prospects
2.7 Conclusions
References
3. Soft Computing in Computer-Integrated Manufacturing (CIM)
Dillip Kumar Mohanta, Surya Narayan Panda and Mahendra Kumar Rath
3.1 Introduction
3.2 Soft Computing
3.3 Benefits of Soft Computing in CIM
3.4 Core Components of Soft Computing
3.5 Soft Computing in Bridging Industry 4.0 and 5.0
3.6 Case Studies and Industrial Applications
3.7 Challenges, Trends, and Future Directions
3.8 Conclusion
Abbreviations
References
4. Soft Computing Applications in Sustainable Manufacturing
Rudra Narayan Mohapatro, Sunita Routray and Ranjita Swain
4.1 Introduction
4.1.1 Overview of Sustainable Manufacturing
4.1.1.1 Principles for the Sustainable Manufacturing
4.1.1.2 Goals of Sustainable Manufacturing
4.1.2 The Role of Digitalization and Smart Technologies in Green Manufacturing
4.1.3 Introduction to Soft Computing
4.1.3.1 Tolerance for Imprecision
4.1.3.2 Tolerance for Uncertainty
4.1.3.3 Approximation Capability
4.1.4 Importance of Soft Computing in Complex, Real-World
Manufacturing Environments
4.2 Overview of Soft Computing Techniques
4.2.1 Fuzzy Logic (FL)
4.2.2 Artificial Neural Networks (ANNs)
4.2.3 Genetic Algorithms (GAs)
4.2.4 Swarm Intelligence (SI)
4.2.5 Hybrid Systems
4.3 Sustainable Manufacturing: Challenges and Goals
4.3.1 Energy Consumption and Emissions
4.3.2 Waste Management and Material Utilization
4.3.3 Lifecycle Assessment and Eco-Design
4.3.4 Supply Chain Sustainability
4.3.5 Real-Time Process Monitoring and Adaptive Control
4.4 Applications of Soft Computing in Sustainable Manufacturing
4.4.1 Process Optimization
4.4.1.1 Use of ANNs and Fuzzy Control in Manufacturing
4.4.1.2 GA and PSO for Multi-Objective Optimization
4.4.2 Quality Control and Fault Detection
4.4.2.1 ANNs for Defect Prediction and Quality Assurance in Real Time
4.4.2.2 Fuzzy Logic in Sensor Data Interpretation to Reduce Rework and Scrap
4.4.3 Resource Management
4.4.3.1 GA and ACO in Optimizing Resource Allocation and Scheduling
4.4.3.2 Fuzzy Models for Decision Support in Water and Energy Use
4.4.4 Supply Chain and Logistics
4.4.4.1 Soft Computing in Green Logistics: Route Optimization, Inventory Management with Minimal Carbon Footprint
4.4.4.2 PSO and Hybrid Methods for Sustainable Supplier Selection
4.4.5 Predictive Maintenance
4.4.5.1 ANN and Fuzzy Inference Systems for Predicting Equipment Failure
4.4.5.2 Minimizing Downtime and Waste through Smart Maintenance Schedules
4.4.6 Lifecycle and Design for Sustainability
4.5 Case Studies
4.5.1 Real-World Example of ANN-Based Energy Optimization in a Machining Process
4.5.1.1 Case Study on ANN-Based Energy Optimization
4.5.1.2 Methodology
4.5.1.3 Results
4.5.1.4 Sustainability Impact
4.5.2 GA for Scheduling in a Green Factory
4.5.3 Case Studies on Fuzzy Logic Controller in a Sustainable Cooling System
4.5.4 Hybrid Models for Minimizing Material Waste in Additive Manufacturing
4.6 Benefits and Limitations
4.7 Future Directions and Research Opportunities
4.7.1 Industry 4.0 + IoT as Enablers of Sustainability, with SC as the Glue
4.7.2 Digital Twins (DTs) with SC Intelligence
4.7.3 Edge/Fog AI for Low-Latency Sustainability Control
4.7.4 Multi-Objective Optimization Under Streaming IIoT Data
4.7.5 SC-Based Decision Support and MCDM for Transformation and Supply Chains
4.7.6 Trustworthy and Explainable AI (XAI) for Industrial CPS
4.7.7 Federated/Continual Learning for Privacy-Preserving,
Low-Carbon Collaboration
4.7.8 Interoperability and Standards for Sustainable I4.0 at Scale
4.7.9 Energy Management Systems (ISO 50001) Augmented by SC + IIoT
4.8 Conclusions
References
5. Sustainable Manufacturing of Agro-Waste Reinforced Aluminum Composites: Machining Optimization and Prediction by Artificial Neural Network
V. Veeranaath and Arun Nallathambi
5.1 Introduction
5.2 Materials and Methods
5.2.1 Development of Composites
5.2.2 Machining of Composites
5.2.3 Optimization and Modeling
5.3 Results and Discussion
5.3.1 Cutting Forces
5.3.2 Specific Cutting Energy
5.3.3 Contact Temperature
5.3.4 Surface Roughness
5.3.5 Optimization
5.3.6 Modeling
5.4 Conclusion
References
6. Leveraging Convolutional Neural Networks for Vision-Based Control in Intelligent Robotic Systems
Sameeha Khan, Syed Faraz Haider Naqvi and Faisal Talib
6.1 Introduction
6.1.1 Background of Intelligent Robotic Systems
6.1.2 The Importance of Visual Control in Robotics
6.1.3 Evolution of Vision-Based Control Techniques
6.1.4 Motivation for Using Deep Learning and CNNs
6.2 Fundamentals of Robotic Visual Control
6.2.1 Overview of Robotic Control System
6.2.2 Types of Robotic Control: Open-Loop vs. Closed-Loop
6.2.3 Principle of Visual Servoing
6.2.4 Robotic Vision
6.2.4.1 Vision Sensors: Cameras, Depth Sensors, RGB-D
6.2.5 Challenges in Vision-Based Control
6.3 Comparative Analysis of CNN Approaches in Vision-Based
Robotic Control
6.4 Application of CNN in Vision-Based Robotic Control
6.4.1 Object Detection for Robotic Navigation
6.4.2 Object Tracking and Following
6.4.3 Scene Understanding and Semantic Segmentation
6.4.4 Case Examples from Industry and Research for Application of CNN in Vision-Based Robotic Control
6.5 Review of Related Work
6.5.1 CNN-Based Visual Servoing: State-of-the-Art
6.5.2 Comparison with Classical Visual Control Pipelines
6.5.3 Limitations in Current Research
6.5.4 Benchmark Datasets Used in Visual Control Research
6.5.5 Summary of Gaps and Opportunities
6.6 Proposed Approach: Lightweight CNN for Visual Control
6.6.1 Rationale for Lightweight CNN Design
6.6.2 Task Selection
6.6.3 Dataset Description (Public Dataset Used)
6.6.4 Data Preprocessing and Augmentation
6.6.5 Model Architecture Overview
6.6.6 Training Setup and Hyperparameters
6.7 Discussion
6.7.1 Benefits of the Planned Method
6.7.2 Applied Uses for Real-World Robotic Systems
6.7.3 Integration with Real Robots (Future Scope)
6.7.4 Deployment Bottlenecks and Challenges
6.8 Future Directions in CNN-Based Visual Control
6.8.1 Combining CNNs with Reinforcement Learning for Adaptive Control
6.8.2 Edge AI and Low-Power Inference for Mobile Robots
6.8.3 Multimodal Perception: Fusion of Vision with Other Sensors
6.8.4 Human-in-the-Loop Learning and Continuous Adaptation
6.8.5 Safety and Ethical Considerations
6.9 Conclusion
6.9.1 Introduction to Key Concepts Discussed
6.9.2 Major Contributions of the Mini Study
6.9.3 Final Observations on Future Opportunities
References
7. Soft Computing in Testing and Analysis Correlation
Alok Kumar
7.1 Introduction
7.2 Details of Soft Computing Approaches
7.2.1 Fuzzy Logic-Based Theory
7.2.2 Neural Networks
7.2.3 Ant Colony Optimization Approaches
7.2.4 Genetic Algorithm
7.2.5 Simulated Annealing
7.2.6 Particle Swarm Optimization Approaches
7.3 Soft Computing Testing of Advanced Machining Processes
7.3.1 Cutting Tool Process
7.3.1.1 Dimension Deviation on Surface Finish
7.3.1.2 Tool Wear Monitoring Condition
7.3.1.3 Cutting Force Analysis
7.3.1.4 Optimization Process
7.3.2 Electromachining
7.3.2.1 Fuzzy Logic Modeling
7.3.2.2 ANN Modeling with Reverse Propagation
7.3.2.3 ANN Modeling with RBF
7.3.3 Additive Manufacturing
7.3.3.1 Performance of ANN Model
7.3.3.2 Optimized 3D Printing Settings
7.3.3.3 Discuss the Effects of the Various Printing Parameters
7.3.3.4 Data Pre- and Post-Processing
7.3.3.5 Gauge Factor Modeling with ANN
7.3.3.6 Development Function with Genetic Algorithm Optimization
7.3.4 Milling Process (Numerical Control)
7.3.4.1 Surface Irregularity
7.3.4.2 Tool Wear Monitoring Condition
7.3.4.3 Cutting Force
7.3.4.4 Process Optimization
7.3.5 Grinding Process
7.3.5.1 Technological Approach for Goal Investigation
7.3.5.2 Evaluation of Parameters by Using the NN
7.3.5.3 Surface Finishing
7.3.5.4 Grinding, Burning, and Wheel Degradation
7.3.5.5 Process Optimization
7.3.6 Drilling Process
7.3.6.1 Dimensional Deviation for Surface Finish
7.3.6.2 Tool Wear Monitoring Condition
7.3.6.3 Force Analysis
7.3.6.4 Process Optimization
7.4 Conclusion
References
8. Life Cycle Assessment: From the Viewpoint of Intelligent
Computing
Sarthak Prasad Sahoo
8.1 Introduction
8.2 Need for Accessing Environmental Impacts
8.3 Objectives
8.4 Fundamentals of Product Life Cycle Assessment
8.4.1 Definition and Scope
8.4.2 LCA Applications
8.4.3 Challenges in Traditional LCA
8.5 Soft Computing and Its Role in LCA
8.5.1 Key Features of Soft Computing
8.5.2 Major Soft Computing Techniques
8.5.3 Role of Soft Computing in LCA
8.5.4 Case Studies
8.6 Future Scopes for LCA
8.7 Conclusion
References
9. Smart Manufacturing Systems: Challenges, Innovation, and Opportunities
Hamid Raza, Mohd Saquib Shamim, Adnan Meraj and Faisal Talib
9.1 Introduction
9.1.1 Overview
9.1.2 Use and Applicability: What Makes Smart Manufacturing Important
9.1.3 The Path Forward
9.2 Digital Threads in Smart Manufacturing System
9.2.1 Digital Thread Application Scenarios
9.2.2 Crucial Technologies of Digital Thread Implementation
9.2.3 Understanding AR and VR in Manufacturing
9.2.3.1 Capabilities of AR/VR in Manufacturing
9.2.3.2 The Role of AR/VR in CAD Design and AI Inclusion
9.2.3.3 Challenge, Opportunity, and Future Trend
9.2.4 Introduction: Understanding Smart Machining
9.2.4.1 Capabilities of Smart Machining
9.2.4.2 Machine Learning in CNC Machining
9.2.4.3 Case Study: Shark Factory and Smart CAM Integration
9.2.4.4 Digital Twin and Virtual Commissioning: Tronrud Engineering
9.2.4.5 Challenges, Opportunities, and Future Trends
9.2.5 Real-Time Quality Control
9.2.5.1 LoT Sensors in Smart Manufacturing
9.2.5.2 AI-Integrated Computer Vision for Robotic Assembly
9.2.5.3 Future Trends
9.2.6 Supportive Technology Includes
9.2.6.1 Big Data and Analytics
9.2.6.2 Digital Twin Technology
9.2.6.3 Cloud Computing
9.2.6.4 Smart Sensor and Processor
9.3 Conclusion
References
10. Assessing the Enablers of IIoT Adoption in Manufacturing Environments: An Integration of BWM and VIKOR
Approaches
Shahzeb Adil and Faisal Talib
10.1 Introduction
10.1.1 Background
10.1.2 Importance of IIoT in Manufacturing
10.1.3 IIoT Adoption in Manufacturing and Role of Enablers
10.1.3.1 Technological Readiness
10.1.3.2 Strategic Alignment
10.1.3.3 Organizational Readiness
10.1.3.4 Cultural Acceptance
10.1.4 Multi-Criteria Decision Making: BWM and VIKOR
10.1.4.1 Best Worst Method (BWM)
10.1.4.2 VIKOR
10.1.5 Motivation
10.1.6 Scope and Objectives
10.1.6.1 Scope
10.1.6.2 Objectives
10.2 Literature Review
10.2.1 Research Gap
10.2.2 Objectives of the Study
10.2.2.1 Focus on SMEs in the Manufacturing Sector
10.2.2.2 Development of a Hybrid BWM-VIKOR Model
10.2.2.3 Incorporation of Technological and Organizational Dimensions
10.2.2.4 Customization for Sectoral and Regional Relevance
10.2.2.5 Empirical Validation Through Expert Judgment
10.3 Enablers and Criteria
10.3.1 Enablers of IIoT Adoption
10.3.2 Evaluation Criteria
10.4 Research Methodology
10.4.1 Research Workflow
10.4.2 Survey
10.4.3 Best Worst Method (BWM)
10.4.3.1 Steps to Perform BWM
10.4.3.2 Optimization Solver
10.4.4 VIKOR Approach
10.4.5 Decision Making and Interpretation
10.5 Results and Discussion
10.5.1 Results from Best Worst Method
10.5.1.1 Selection of Best and Worst Criteria
10.5.1.2 Pairwise Comparison
10.5.1.3 Weight Calculation
10.5.2 Results from VIKOR Analysis
10.5.2.1 Ranking of Enablers from Survey (Decision Matrix)
10.5.2.2 Normalization of Decision Matrix
10.5.2.3 Weighted Normalization of Normalized Matrix
10.5.2.4 Utility and Regret Measures
10.5.2.5 VIKOR Index (Q)
10.5.2.6 Ranking
10.5.3 Compromise Solution
10.5.4 Discussion
10.6 Conclusion
10.6.1 Limitations
10.6.2 Future Scope
References
11. Soft Computing in Intelligent Welding Manufacturing System and Robotic Welding
Sandip Kunar, Jagadeesha T., Ajithkumar Sitharaj, Sridevi Gamini, Gurudas Mandal and S. D. V. V. S. Bhimeshwar Reddy
11.1 Introduction
11.2 Technical Composition of IWMS
11.2.1 Overview of the Welding Manufacturing Procedure
11.2.2 Performing Requirements and Model Design of IWMS
11.2.3 Intelligent Packaging in IWMS
11.2.3.1 Reactive Agent of IWMS
11.2.3.2 Deliberative Agent of IWMS
11.2.3.3 Compound Agent of IWMS
11.2.3.4 Target-Based Agent of IWMS
11.3 The Multi-Agent Collaboration Mechanism of IWMS
11.3.1 The Structure of Agent Alliance System
11.3.2 Central Control Agent and Collaborative Agent of IWMS
11.4 Unit Design and Functional Execution of Multi-Agents in the IWMS
11.4.1 Laser Vision Agent
11.4.2 Welding Start Point Guide Agent
11.4.3 Weld Seam Tracking Agent
11.4.4 Welding Voltage and Current Monitoring Agent
11.4.5 Arc Sound Monitoring Agent
11.4.6 Molten Pool Monitoring Agent
11.4.7 Welding Power Control Agent
11.5 An IWMS Experimental System Using MAS and IoT Patterns
11.5.1 Intelligent Welding Process Monitoring System
11.5.2 Weld Forming Characteristic Extraction Utilizing an IoT-MAS Framework
11.6 Robotic Welding
11.7 Conclusions
References
Index

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