Cutting tool condition monitoring using surface texture via neural network
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Date
2003
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Abstract
For defining surface finish and monitoring tool wear is essential for optimisation of machining parameters and performing automated manufacturing systems. There is very close relationship between tool wear and surface finish parameters as surface roughness (Ra) and maximum depth of profile (Rt). The machined surface reflects the rate of tool wear and the plot of surface provides reliable information about tool condition. In this paper an approach for estimating Ra and Rt in milling process using the artificial neural networks is proposed. Feed-forward multi-layered neural networks, trained by the back-propagation algorithm are used. In training phase seven input parameters (v, f, d, Fx, Fy, Fz and Vb) and two output parameters are used and the network architecture is as 7×6×6×6×2. It was found that the ANN results are very close to the experimental results. The developed model can be used to define the quality of surface finish in tool condition monitoring systems.