Buldum, Berat BarisSık, AydınAkdağlı, AliBiçer, Mustafa BerkanAldaş, Mersin KemalÖzkul, İskender13.07.20192019-07-1613.07.20192019-07-1620170025-5300https://doi.org/10.3139/120.111088https://hdl.handle.net/20.500.12451/4196This contribution presents an approach for the modeling and prediction of surface roughness in the turning of AZ91D magnesium alloys using an artificial neural network. The experiments were conducted with CCGT, DCGT and VCGT cutting tools under minimum quantity lubrication and dry machining conditions. AZ91D alloys were machined at different cutting speeds and feed rates, and the depth of cut was kept constant. 15 out of 18 experimental data points were used for the training of the artificial neural network model and the remaining 3 were used for the testing process. The average percentage error was calculated as 0.000815 % and 0.663 % for training and testing, respectively. The model and target results were found to have extremely low error rates.eninfo:eu-repo/semantics/closedAccessMagnesium AlloyAZ91DArtificial Neural NetworkSurface RoughnessProperty PredictionArticle591091692010.3139/120.111088WOS:000415695400013N/A