Optimization of machining parameters using genetic algorithm and experimental validation for end-milling operations |
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Authors: | P Palanisamy I Rajendran S Shanmugasundaram |
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Affiliation: | (1) Department of Mechanical Engineering, Kumaraguru College of Technology, Coimbatore, Tamil Nadu, 641006, India;(2) Department of Mechanical Engineering, Bannari Amman Institute of Technology, Sathyamangalam, Tamil Nadu, 638401, India;(3) Government College of Technology, Coimbatore, Tamil Nadu, 641013, India |
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Abstract: | Optimization of cutting parameters is valuable in terms of providing high precision and efficient machining. Optimization
of machining parameters for milling is an important step to minimize the machining time and cutting force, increase productivity
and tool life and obtain better surface finish. In this work a mathematical model has been developed based on both the material
behavior and the machine dynamics to determine cutting force for milling operations. The system used for optimization is based
on powerful artificial intelligence called genetic algorithms (GA). The machining time is considered as the objective function
and constraints are tool life, limits of feed rate, depth of cut, cutting speed, surface roughness, cutting force and amplitude
of vibrations while maintaining a constant material removal rate. The result of the work shows how a complex optimization
problem is handled by a genetic algorithm and converges very quickly. Experimental end milling tests have been performed on
mild steel to measure surface roughness, cutting force using milling tool dynamometer and vibration using a FFT (fast Fourier
transform) analyzer for the optimized cutting parameters in a Universal milling machine using an HSS cutter. From the estimated
surface roughness value of 0.71 μm, the optimal cutting parameters that have given a maximum material removal rate of 6.0×103 mm3/min with less amplitude of vibration at the work piece support 1.66 μm maximum displacement. The good agreement between the
GA cutting forces and measured cutting forces clearly demonstrates the accuracy and effectiveness of the model presented and
program developed. The obtained results indicate that the optimized parameters are capable of machining the work piece more
efficiently with better surface finish. |
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Keywords: | Genetic algorithms Milling Cutting force Surface roughness Tool life Machining time |
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