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- W2811380078 abstract "In this work, we develop a modeling based on an Artificial Neural Network (ANN) and Multiple Linear Regression (MLR) to predict the machining force components generated during hard turning of a bearing steel with CBN cutting tool. The inputs of the ANN model were the cutting parameters (cutting speed, feed and depth-of-cut) and the workpiece hardness. The network training is performed by using experimental data. The optimal network architecture is determined after several simulations by Matlab Neural Network Toolbox. Back-propagation by Bayesian Regularization in combination with Levenberg–Marquardt algorithm is employed for training. The ANN approach is suitable to estimate the machining force components such as feed-force, radial-force and tangential-force; for this purpose, the results are compared to those obtained by experiment, and the maximum average MAPE value of 4.58 % was obtained for the machining force prediction. Also, the ANN results were compared to those obtained by MLR model. It was shown that the ANN model produced more successful results.DOI: http://dx.doi.org/10.5755/j01.mech.24.3.19146" @default.
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- W2811380078 date "2018-06-28" @default.
- W2811380078 modified "2023-10-14" @default.
- W2811380078 title "Modeling of Machining Force in Hard Turning Process" @default.
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- W2811380078 doi "https://doi.org/10.5755/j01.mech.24.3.19146" @default.
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