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Multiple Optimizations for Selection of Machining Parameters of Inconel-718 Material Turning Process


Affiliations
1 Dept of Mechatronics Engg., Kumaraguru College of Technology, Coimbatore, India
2 Dept of Production Engg., National Institute of Technology, Tiruchirapalli, India
     

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Determination of cutting parameters for tough and hard material is very important for the process planner to achieve the economic machining process. The paper proposes a new optimization technique based on genetic algorithms (GA) to optimize the objectives like minim.um surface roughness, power required and cutting force and maximum tool life. Many researchers concentrate the single objective to optimize the process parameters, this paper presents a new methodology to optimize the process parameters for each objective each time one objective will optimize and other objective will be treated as constraints. Experimental results proved that the effectiveness of proposed genetic algorithm based multiple optimizations solving this machining problem.
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  • Multiple Optimizations for Selection of Machining Parameters of Inconel-718 Material Turning Process

Abstract Views: 215  |  PDF Views: 0

Authors

J. S. Senthilkumaar
Dept of Mechatronics Engg., Kumaraguru College of Technology, Coimbatore, India
R. Saravanan
Dept of Mechatronics Engg., Kumaraguru College of Technology, Coimbatore, India
P. Asokan
Dept of Production Engg., National Institute of Technology, Tiruchirapalli, India

Abstract


Determination of cutting parameters for tough and hard material is very important for the process planner to achieve the economic machining process. The paper proposes a new optimization technique based on genetic algorithms (GA) to optimize the objectives like minim.um surface roughness, power required and cutting force and maximum tool life. Many researchers concentrate the single objective to optimize the process parameters, this paper presents a new methodology to optimize the process parameters for each objective each time one objective will optimize and other objective will be treated as constraints. Experimental results proved that the effectiveness of proposed genetic algorithm based multiple optimizations solving this machining problem.