Due to the expansion of milling process with ball end mill in various branches of industry it became necessary for this process to be optimized. For this purpose it is necessary to identify the parameters that influence the process and establish their value for witch the results obtained to be the maximum in terms of qualitative and quantitative. Roughness of the surface machined can be considered as an important element that reflects the degree of successful optimization of this process. In order to solve the problems relating to the analysis and estimation of the surface roughness variation in ball end milling of C45 material with tool tilt angle, in this paper it was designed an experimental methodology followed by analysis of experimental data and estimation of surface roughness variation. The experimental research methodology presented in this paper can be extrapolated and used in a large number of processes.
This paper is intended to create an artificial neural network capable of generating new values for the roughness on the basis of experimentally obtained data bases. Experimentally you will measure the roughness of the flat surfaces processed with the toroidal milling, the process factors being the input neurons of the neural network, following the roughness values being the output neurons. It aims to modify the input neurons from the same neural network and generate new roughness values.
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