PREDICTION AND OPTIMIZATION OF SURFACE ROUGHNESS FOR END MILLING OPERATION USING ARTIFICIAL NEURAL NETWORKS AND GENETIC ALGORITHM

Authors

  • Kalidass S Department of Mechanical Engineering, Dr. N. G. P. Institute of Technology, Coimbatore, Tamil Nadu-641048, India
  • Palanisamy P Department of Mechanical Engineering, Dr.Navalar Nedunzhezhian College of Engineering, Tholudur, Tamil Nadu-606303, India
  • Muthukumaran V Department of Mechanical Engineering, Kumaraguru college of Technology, Coimbatore, Tamil Nadu-641035,India

Keywords:

Tool Geometry, Artificial Neural Networks, Fractional Factorial, Genetic Algorithm (GA), Surface Roughness , End milling

Abstract

This paper presents the work related the development of neural network model for predicting surface roughness and optimization of the process parameters for minimizing surface roughness using Genetic Algorithm. The process parameters chosen for this study are helix angle of tool geometry, spindle speed, feed rate, and depth of cut, while the output parameter is surface roughness. The experiments were conducted based on design of experiments using fractional factorial with 125 runs. The material and tool selected for this study is AISI 304 Austenitic Stainless Steel (AISI 304) and uncoated solid carbide end mill cutter respectively. Using the experimental data, feed-forward back propagation neural network model was developed and it was trained using the Levenberg– Marquardt algorithm. It was observed that the ANN model based on network 4-12-1 predicted surface roughness more accurately. To ensure optimization, a mathematical model was also developed to correlate the process parameters with surface roughness. A source code was developed in MATLAB to carry out the optimization. The optimized process parameters gave a value of 0.75132 µm for surface roughness

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Published

2013-03-01

How to Cite

[1]
Kalidass S, Palanisamy P, and Muthukumaran V, “PREDICTION AND OPTIMIZATION OF SURFACE ROUGHNESS FOR END MILLING OPERATION USING ARTIFICIAL NEURAL NETWORKS AND GENETIC ALGORITHM”, JME, vol. 8, no. 1, pp. 028–037, Mar. 2013.

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