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  • Küçük Resim Yok
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    Application of Artificial Intelligent to Predict Surface Roughness
    (WILEY-BLACKWELL, 2014) Asilturk, I.
    This article proposes for predicting the surface roughness of AISI 1040 steel material using the artificial intelligent. Cutting speed, feed rate, depth of cut, and nose radius have been taken into consideration as input factors and corresponding surface roughness values (R-a, R-t) as output. A series of experiments have been carried out in accordance with a full factorial design on the CNC lathe to obtain the data used for the training and testing of an artificial neural network (ANN). The developed MATLAB TM interface was used to predict surface roughness. Multilayer perceptron structure, which is a kind of feed forward ANNs, is applied to model and prediction of the surface roughness in turning operations. The number of iterations used was 20,000. MSE = 1E-4 and R-2 = 0.9991 were achieved using the developed ANN Model. The obtained results indicate that the ANN algorithm coupled with back propagation neural network is an efficient and accurate method in predicting of surface roughness in turning.
  • Küçük Resim
    Öğe
    Prediction of cutting forces and surface roughness using artificial neural network (ANN) and support vector regression (SVR) in turning 4140 steel
    (TAYLOR & FRANCIS LTD, 2012) Asilturk, I.; Kahramanli, H.; El Mounayri, H.
    In the present study, the prediction of cutting forces and surface roughness was carried out using neural networks and support vector regression (SVR) with six inputs, namely, three axis vibrations of the tool holder and cutting speed, feedrate and depth of cut. The data obtained by experimentation are used to construct predictive models. A feedforward backpropagation neural network and SVR have been selected for modelling. The coefficient of determination (R-2), mean absolute prediction error and root mean square error were calculated for each method, and these values served as a measure of prediction precision. We carried out comparison of the prediction accuracy of artificial neural networks and SVR. Comparison of the two models indicates that both models have successful performance. Experimental results are provided to confirm the effectiveness of this approach.