Prediction of Surface Roughness by Hybrid Artificial Neural Network and Evolutionary Algorithms in End Milling

Document Type : Research Paper


1 Department of Industrial Engineering, Mazandaran University of Science and Technology, Mazandaran, Iran

2 Mazandaran University of Science and Technology, Mazandaran, Iran

3 Mechanical Engineering Department, Tehran University, Tehran, Iran


Machining processes such as end milling are the main steps of production which have major effect on the quality and cost of products. Surface roughness is one of the considerable factors that production managers tend to implement in their decisions. In this study, an artificial neural network is proposed to minimize the surface roughness by tuning the conditions of machining process such as cutting speed, feed rate and depth of cut. The proposed network is tested by many test problems of Ghani et al.[1] study and the weights of network are optimized by using three meta-heuristics, genetic algorithm (GA), imperialist competitive algorithm (ICA). The results show the efficiency and accuracy of the proposed network.


Main Subjects

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