Soft Computing in Smart Manufacturing and Materials explains the role of soft computing in the manufacturing industries. It presents the techniques, concepts and design principles behind smart soft computing, and describes how they can be applied in the development and manufacture of smart materials. It provides perspectives for design and commissioning of intelligent applications, including in health care, agriculture, and production assembly, and reviews the latest intelligent technologies and algorithms related to the methodologies of monitoring and mitigation of sustainable engineering.
1. Introduction - p. xxi 2. Chapter 1. Introduction to smart soft computing in engineering: Embracing intelligence and efficiency - p. 1 3. Chapter 2. An enhanced approach for soft computing-based manufacturing optimization using nonlinear intuitionistic fuzzy programming techniques - p. 11 4. Chapter 3. Components of smart materials - p. 47 5. Chapter 4. Recent trends and innovation in manufacturing with artificial intelligence/machine learning technologies - p. 75 6. Chapter 5. Next-gen distributed denial-of-service detection and mitigation in software-defined networking using hybrid machine learning approach - p. 97 7. Chapter 6. Soft computing in manufacturing: Trends, innovations, and future prospects - p. 135 8. Chapter 7. Artificial intelligence in industrial applications - p. 157 9. Chapter 8. Soft computing and system engineering for smart manufacturing - p. 169 10. Chapter 9. Application of ANFIS for predicting cutting forces, material removal rate, and surface roughness in dry grinding of Ti–6Al–4V alloy utilizing a 2% CNT nanogrinding tool - p. 193 11. Chapter 10. Testing and analyzing genetic algorithms: Advancing ethical intelligence through integrating interpretability, human-centric design, and continuous learning in smart soft computing systems - p. 217 12. Chapter 11. Product life-cycle assessment using machine learning - p. 247 13. Chapter 12. Ultralow power communication: Challenges and machine learning solutions - p. 261 14. Index - p. 273
Soft Computing in Smart Manufacturing and Materials explains the role of soft computing in the manufacturing industries. It presents the techniques, concepts and design principles behind smart soft computing, and describes how they can be applied in the development and manufacture of smart materials. It provides perspectives for design and commissioning of intelligent applications, including in health care, agriculture, and production assembly, and reviews the latest intelligent technologies and algorithms related to the methodologies of monitoring and mitigation of sustainable engineering.
1. Introduction - p. xxi 2. Chapter 1. Introduction to smart soft computing in engineering: Embracing intelligence and efficiency - p. 1 3. Chapter 2. An enhanced approach for soft computing-based manufacturing optimization using nonlinear intuitionistic fuzzy programming techniques - p. 11 4. Chapter 3. Components of smart materials - p. 47 5. Chapter 4. Recent trends and innovation in manufacturing with artificial intelligence/machine learning technologies - p. 75 6. Chapter 5. Next-gen distributed denial-of-service detection and mitigation in software-defined networking using hybrid machine learning approach - p. 97 7. Chapter 6. Soft computing in manufacturing: Trends, innovations, and future prospects - p. 135 8. Chapter 7. Artificial intelligence in industrial applications - p. 157 9. Chapter 8. Soft computing and system engineering for smart manufacturing - p. 169 10. Chapter 9. Application of ANFIS for predicting cutting forces, material removal rate, and surface roughness in dry grinding of Ti–6Al–4V alloy utilizing a 2% CNT nanogrinding tool - p. 193 11. Chapter 10. Testing and analyzing genetic algorithms: Advancing ethical intelligence through integrating interpretability, human-centric design, and continuous learning in smart soft computing systems - p. 217 12. Chapter 11. Product life-cycle assessment using machine learning - p. 247 13. Chapter 12. Ultralow power communication: Challenges and machine learning solutions - p. 261 14. Index - p. 273
