ITM Web Conf.
Volume 15, 2017II International Conference of Computational Methods in Engineering Science (CMES’17)
|Number of page(s)||8|
|Section||Computational And Artificial Intelligence|
|Published online||15 December 2017|
Artificial neural network modelling of cutting force components in milling
1 Lublin University of Technology, Faculty of Mechanical Engineering, Department of Production Engineering, Nadbystrzycka 36, 20-618 Lublin, Poland,
2 Lublin University of Technology, Faculty of Management, Department of Organisation of Enterprises, Nadbystrzycka 38, 20-618 Lublin, Poland
3 Lublin University of Technology, Faculty of Mechanical Engineering, Nadbystrzycka 36, 20-618 Lublin, Poland
* Corresponding author: email@example.com
The following paper will give an account of experimental tests and simulation of the cutting force components Fx, Fy and Fz in down milling of AZ91D magnesium alloy. The milling operation employed two milling cutters with a different helix angle, λs = 20° and λs = 50°, and was conducted with changeable milling machining parameters: cutting speed, feed per tooth, axial depth of cut. The simulation part of the study was conducted in Statistica software environment with the application of Multi-Layered Perceptron neural network architecture, and using a “black box” approach, which guarantees a good fit of input and output data obtained from the experimental tests.
© The Authors, published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (http://creativecommons.org/licenses/by/4.0/).
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