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Sustainable Machining Process Optimization: Predictive Modeling With Multi-Objective Ant Colony Algorithms
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Author(s): Mahendra Kumar B. (Department of MCA, Dayananda Sagar College of Engineering, Bengaluru, India), K. S. Shreenidhi (Department of Biotechnology, Rajalakshmi Engineering College, Chennai, India), Harishchander Anandaram (Department of Artificial Intelligence, Amrita Vishwa Vidyapeetham, Coimbatore, India), Sampath Velpula (Department of Mechanical Engineering, Vignana Bharathi Institute of Technology(VBIT), Medchal, India)and R. Gukendran (Department of Mechanical Engineering, Kongu Engineering College, Erode, India)
Copyright: 2025
Pages: 24
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.ch011
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Abstract
Sustainable machining practices are essential for an effective machining process with minimum impact on the environment. In this respect, this chapter puts forward the utilization of multi-objective ACO techniques applied to predictive modeling on sustainable machining processes. In this research, ACO has been utilized to study the conflicting objectives of energy consumption, tool wear, surface quality, and production time to optimize the parameters for machining. Sustainably machining problems are discussed in detail and further go on to describe ACO algorithms: simulating foraging behavior of ants to identify good solutions. Then, case studies are presented, demonstrating how ACO can simultaneously minimize environmental impact while improving machining performance. The results underline potential resource-efficient manufacturing by way of minimization and decision making for waste with ACO-induced sustainable industrial processes.
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