Abstract—By optimization of various parameters of CNC milling process like spindle speed, feed rate and depth of cut, Improvement can be achieved in surface finishing. Various methods are used for predict surface roughness in CNC milling machine. Here Artificial Neural Network has been implemented for better and nearest result. By using this paper, mathematical model can be developed easily for milling process. Number of experiments have been done by using Hy-tech CNC milling machine. Conclusion from Taguchi method, Surface roughness is most influenced by Feed rate followed by spindle speed and lastly depends on depth of cut. Predicted surface roughness has been obtained, average percentage error is calculated by ANN method. The mathematical model is developed by using Artificial Neural Network (ANN) technique shows the higher accuracy is achieved which is feasible and more efficient in prediction of surface roughness in CNC milling. The result from this paper is useful to be implemented in manufacturing industry to reduce time and cost in surface roughness prediction.

Index Terms—CNC milling, ANN, Surface roughness

Cite: Ravikumar D Patel, Nigam V Oza, and Sanket N Bhavsar, "Prediction of Surface Roughness in CNC Milling Machine by Controlling Machining Parameters Using ANN ," International Journal of Mechanical Engineering and Robotics Research, Vol.3, No.4, pp. 353-359, October 2014.