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Integrating Machine Learning and Computational Intelligence for Green Manufacturing Processes
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Author(s): P. Chitra (Department of Computer Science and Engineering, SRM Institute of Science and Technology, Chennai, India), Rao P. B. V. Raja (Shri Vishnu Engineering College for Women, India), A. Ananthi (Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, India), G. Saritha (Sri Sai Ram Institute of Technology, India), A. Balasuadhakar (AMET University, India)and Sampath Boopathi (Muthayammal Engineering College, India)
Copyright: 2025
Pages: 28
Source title:
Using Computational Intelligence for Sustainable Manufacturing of Advanced Materials
Source Author(s)/Editor(s): Kamalakanta Muduli (Papua New Guinea University of Technology, Papua New Guinea), Bikash Ranjan Moharana (Papua New Guinea University of Technology, Papua New Guinea), Steve Korakan Ales (Papua New Guinea University of Technology, Papua New Guinea)and Dillip Kumar Biswal (Aryan Institute of Engineering and Technology, Bhubaneswar, India)
DOI: 10.4018/979-8-3693-7974-5.ch009
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Abstract
The chapter is focused on integrating machine learning and computational intelligence into green manufacturing processes. ML and CI offer data-driven solutions toward industries strive for reduced environmental impacts through resource usage, energy consumption, and waste reduction, among others. This chapter will focus on some very prominent algorithms, such as neural networks, reinforcement learning, and fuzzy logic, and their applications in predictive maintenance, process optimization, and supply chain management for sustainability. The chapter relates the integration of ML and CI in achieving eco-friendly manufacturing goals—reduction of carbon footprint and improvement in operational efficiency—through case studies and practical examples. It discusses the role played by digital twins, IoT integration, and AI-driven decision-making in enabling adaptive and resilient manufacturing systems. The chapter is concluded by future trends and challenges to implement these technologies on a larger scale for the transformation of industry in a sustainable way.
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