ijiMinds

(ISSN: 2349-2082)

International Journal For Ignited Minds
 Published Article
 Title:
Optimization of Cutting Parameters for Milling Operation using Genetic Algorithm technique through MATLAB
 Authors:
Harsha A M , Ramesh C G
 Abstract:
In any manufacturing operation, machining is the critical operation involves maintenance, cost and tool replacement. Change of tool cutting is generally time consuming operation in any automated machining process. So, one of the nontraditional technique called Genetic Algorithm (GA) based on MATLAB- Ga tool has been used for optimizing machining parameters and to reduce unit cost of a component during milling operation. The main objective of the project work is to check whether MATLAB – Ga tool will help production and design operations as well as to optimize machining parameters and also to reduce the unit cost. The work has been carried out to optimize the machining parameters during milling and to reduce the unit cost of product. For this, initially a known problem is checked with MATLAB. A known problem whose sum of squares is 100 and designed optimized for 3 variables. Next, in a similar way, standard equation is coded with MATLAB function considering three constraint functions like force, power and surface finish. Finally, first experiment carried out by considering two variables speed and feed and second experiment is done with three variables considering speed, feed and depth of cut. Ga tool also shows the convergence in the graph along with listing of the variables as execution proceeds further. The objective values are found to be Rs 443 by considering speed and feed whose optimized values are 120rpm and 0.28mm/min respectively. Similarly in second experiment objective values is found to be Rs 448.36 and optimum parameters value such as speed, feed and depth of cut as 120rpm, 0.304mm/min and 10mm respectively. The obtained results from the GA method are compared with Particle Swarm Optimization method which is popularly known as PSO where, GA gives superior result than PSO method.
 Pages:
168-177
 Keywords:
Optimization, Machining parameters, MATLAB, Genetic Algorithm.
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