@article { author = {Rezaeian, J. and Taheri, A. and Haghaiegh, S.}, title = {Prediction of Surface Roughness by Hybrid Artificial Neural Network and Evolutionary Algorithms in End Milling}, journal = {Iranian Journal of Mechanical Engineering Transactions of the ISME}, volume = {15}, number = {1}, pages = {39-47}, year = {2014}, publisher = {Iranian Society of Mechanical Engineering}, issn = {1605-9727}, eissn = {}, doi = {}, abstract = {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.}, keywords = {End milling,genetic algorithm,Imperialist Competitive Algorithm,Surface roughness,Artificial neural network}, url = {https://jmee.isme.ir/article_19600.html}, eprint = {https://jmee.isme.ir/article_19600_67ddf4b08c88eba4ae76f8068090c0d0.pdf} }