Soft Computing in Materials Development and its Sustainability in the Manufacturing Sector

Amar Patnaik, Vikas Kukshal, Pankaj Agarwal, Ankush Sharma, Mahavir Choudhary

• Publisher: CRC Press
• Year: 2022
• Print ISBN: 9780367723583
• eBook ISBN: 9781000629804
• Edition: 1
• Topic: Computer Science
• Subtopic: Industrial Engineering
• Index: Computer Science

This book focuses on the application of soft computing in materials and manufacturing sectors with the objective to offer an intelligent approach to improve the manufacturing process, material selection and characterization techniques for developing advanced new materials. It unveils different models and soft computing techniques applicable in the field of advanced materials and solves the problems to help the industry and scientists to develop sustainable materials for all purposes. The book focuses on the overall well-being of the environment for better sustenance and livelihood. Firstly, the authors discuss the implementation of soft computing in the various areas of engineering materials. They also review the latest intelligent technologies and algorithms related to the state-of-the-art methodologies of monitoring and effective implementation of sustainable engineering practices. Finally the authors examine the future generation of sustainable and intelligent monitoring techniques beneficial for manufacturing, and cover novel soft computing techniques for the purpose of effective manufacturing processes at par with the standards laid down by the International Standards of Organization (ISO). This book is intended for academics and researchers from all the fields of engineering interested in joining interdisciplinary initiatives on soft computing techniques for advanced materials and manufacturing.

Table of contents

• Cover
• Half Title
• Series Page
• Title Page
• Copyright Page
• Table of Contents
• Preface
• Acknowledgements
• Editors
• List of Contributors
• Chapter 1 Predictive Maintenance of Industrial Rotating Equipment Using Supervised Machine Learning
• Chapter 2 Predictive Approach to Creep Life of Ni-based Single Crystal Superalloy Using Optimized Machine Learning Regression Algorithms
• Chapter 3 Artificial Neural Networks Based Real-time Modelling While Milling Aluminium 6061 Alloy
• Chapter 4 Smart Techniques of Microscopic Image Analysis and Real-Time Temperature Dispersal Measurement for Quality Weld Joints
• Chapter 5 Industrial Informatics Cache Memory Design for Single Bit Architecture for IoT Approaches
• Chapter 6 The Bending Behavior of Carbon Fiber Reinforced Polymer Composite for Car Roof Panel Using ANSYS 21
• Chapter 7 Sustainable Spare Parts Inventory and Cost Control Management Using AHP-Based Multi-Criterion Framework: Perspective on Petroleum & Fertilizer Industries
• Chapter 8 Simulation of Deployment of Inflatable Structures Through Uniform Pressure Method
• Chapter 9 Experimental and Machine Learning Approach to Evaluate the Performance of Refrigerator and Air Conditioning Using TiO2 Nanoparticle
• Chapter 10 Numerical and Experimental Investigation on Thinning in Single-Point Incremental Sheet Forming
• Chapter 11 Smart Manufacturing: Opportunities and Challenges Overcome by Industry 4.0
• Chapter 12 Multi-Response Optimization of Input Parameters in End Milling of Metal Matrix Composite Using TOPSIS Algorithm
• Chapter 13 Numerical and Experimental Investigation of Additive Manufactured Cellular Lattice Structures
• Chapter 14 Wear Measurement by Real-Time Condition Monitoring Using Ferrography
• Chapter 15 Design, Modelling and Comparative Analysis of a Horizontal Axis Wind Turbine
• Index