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Optimization of Abrasive Jet Machining Processes Using Evolutionary Algorithms: A Computational Approach
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Author(s): T. Kumaresan (Department of Mechanical Engineering, PSG Polytechnic College, Coimbatore, India), Shrikant Joshi (Brahmadevdada Mane Polytechnic, Solapur, India), Nachimuthu Somasundaram (Department of Mechanical Engineering, Rathinam Technical Campus, Coimbatore, India), Shreenidhi K. S. (Department of Biotechnology, Rajalakshmi Engineering College, Chennai, India)and Harishchander Anandaram (Department of Artificial Intelligence, Amrita Vishwa Vidyapeetham, Coimbatore, 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.ch012
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Abstract
Abrasive Jet Machining is a non-traditional machining process with the considerable versatility of use, mostly in cutting, cleaning, and deburring hard and brittle materials. In AJM, optimal parameters involve such things as abrasive flow rate, pressure, and standoff distance, all of which are very important for attaining the appropriate performance metrics with respect to material removal rate (MRR) and surface finish. It applies evolutionary algorithms, specifically Genetic Algorithms (GA) and Particle Swarm Optimization (PSO), to the optimization of AJM processes. The evolutionary algorithms may efficiently locate optimal parameter combinations from computer simulations of various machining conditions into significant reductions in energy, wear, and increases in accuracy in machining. The computational approach given below provides a robust framework for achieving precision in AJM, beating traditional optimization techniques. Experimental verification has demonstrated the effectiveness of proposed EA-based models in optimizing efficiency in AJM.
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