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

Authors:
Azlan Mohd Zain;Habibollah Haron;Safian Sharif
Affiliations:
Department of Modelling and Industrial Computing, Faculty of Computer Science and Information System, Universiti Teknologi Malaysia, 81310 UTM Skudai Johor, Malaysia;Department of Modelling and Industrial Computing, Faculty of Computer Science and Information System, Universiti Teknologi Malaysia, 81310 UTM Skudai Johor, Malaysia;Department of Manufacturing and Industrial Engineering, Faculty of Mechanical Engineering, Universiti Teknologi Malaysia, 81310 UTM Skudai Johor, Malaysia
Venue:
Expert Systems with Applications: An International Journal
Year:
2010

Citing 1
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Kernel based regression and genetic algorithms for estimating cutting conditions of surface roughness in end milling machining process

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Abstract

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@mm 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.