Universiti Teknologi Malaysia Institutional Repository

Application of GA to optimize cutting conditions for minimizing surface roughness in end milling machining process

Mohd. Zain, Azlan and Haron, Habibollah and Sharif, Safian (2010) Application of GA to optimize cutting conditions for minimizing surface roughness in end milling machining process. Expert Systems with Applications, 37 (6). pp. 4650-4659. ISSN 0957-4174

Full text not available from this repository.

Official URL: http://dx.doi.org/10.1016/j.eswa.2009.12.043


This study is carried out to observe the optimal effect of the radial rake angle of the tool, combined with speed and feed rate cutting conditions in influencing the surface roughness result. In machining, the surface roughness value is targeted as low as possible and is given by the value of the optimal cutting conditions. By looking at previous studies, as far as they have been reviewed, it seems that the application of GA optimization techniques for optimizing the cutting conditions value of the radial rake angle for minimizing surface roughness in the end milling of titanium alloy is still not given consideration by researchers. Therefore, having dealt with radial rake angle machining parameter, this study attempts the application of GA to find the optimal solution of the cutting conditions for giving the minimum value of surface roughness. By referring to the real machining case study, the regression model is developed. The best regression model is determined to formulate the fitness function of the GA. The analysis of this study has proven that the GA technique is capable of estimating the optimal cutting conditions that yield the minimum surface roughness value. With the highest speed, lowest feed rate and highest radial rake angle of the cutting conditions scale, the GA technique recommends 0.138 µm as the best minimum predicted surface roughness value. This means the GA technique has decreased the minimum surface roughness value of the experimental sample data, regression modelling and response surface methodology technique by about 27%, 26% and 50%, respectively.

Item Type:Article
Uncontrolled Keywords:genetic algorithm, milling, optimization, surface roughness
Subjects:T Technology > TJ Mechanical engineering and machinery
Divisions:Mechanical Engineering
ID Code:13225
Deposited By: S.N.Shahira Dahari
Deposited On:25 Jul 2011 10:01
Last Modified:03 Mar 2017 05:40

Repository Staff Only: item control page