The neural network is one of the soft computing methods usually used to solve the problems that could not be done by traditional computation. The operation of the CNC machine requires regular adjustment of the input parameters in order to produce high quality products and to keep machines in good conditions for a long time. However, some parameters can't be computed directly from the mathematic formulas but can only be calculated approximately by some softcomputing methods such as neural networks. This article presents the neural network design to calculate optimal feed speeds of the milling process in the CNC machines based on the parameters of surface roughness, tool diameters and the depth of a cut
Metal cutting is one of the most significant manufa cturing processes in the area of material remova...
This paper presents the model for milling AZ91HP magnesium alloy with TiAlN coated carbide end mill....
In the metal cutting process of machine tools, the quality of the surface roughness of the product i...
The purpose of this study was to design and test an intelligent computer software developed with the...
Abstract Machining at relatively high speed perform differently than when traditionally cutting spee...
Machinability data is a crucial factor affecting manufacturing cost and quality. Two artificial neur...
This thesis discuss the Optimization of surface roughness in milling using Artificial Neural Network...
We describe a device which uses a neural network to generate part-programs for milling, drilling and...
The present study is focused to investigate the effect of the various machining input parameters suc...
In this thesis, Artificial Neural Network (ANN) technique is used to model and simulate the Turning ...
The purpose of this work is to investigate and develop optimization techniques used for single pass ...
In this paper, an experimental study was conducted to determine the effect of different cutting para...
Surface roughness and cutting forces are considered as important factors to determine machinability ...
The purpose of the presented paper is to show how with the help of artifi cial Neural Network (NN) t...
Abstract:-Recent developments, focused on the optimization of machining processes, through an effect...
Metal cutting is one of the most significant manufa cturing processes in the area of material remova...
This paper presents the model for milling AZ91HP magnesium alloy with TiAlN coated carbide end mill....
In the metal cutting process of machine tools, the quality of the surface roughness of the product i...
The purpose of this study was to design and test an intelligent computer software developed with the...
Abstract Machining at relatively high speed perform differently than when traditionally cutting spee...
Machinability data is a crucial factor affecting manufacturing cost and quality. Two artificial neur...
This thesis discuss the Optimization of surface roughness in milling using Artificial Neural Network...
We describe a device which uses a neural network to generate part-programs for milling, drilling and...
The present study is focused to investigate the effect of the various machining input parameters suc...
In this thesis, Artificial Neural Network (ANN) technique is used to model and simulate the Turning ...
The purpose of this work is to investigate and develop optimization techniques used for single pass ...
In this paper, an experimental study was conducted to determine the effect of different cutting para...
Surface roughness and cutting forces are considered as important factors to determine machinability ...
The purpose of the presented paper is to show how with the help of artifi cial Neural Network (NN) t...
Abstract:-Recent developments, focused on the optimization of machining processes, through an effect...
Metal cutting is one of the most significant manufa cturing processes in the area of material remova...
This paper presents the model for milling AZ91HP magnesium alloy with TiAlN coated carbide end mill....
In the metal cutting process of machine tools, the quality of the surface roughness of the product i...