Cutting Tool Condition Monitoring Using Surface Texture via Neural Network

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 (R_a,) and maximum depth of profile (R_t). The machined surface reflects the rate of tool wear and the plot of surface pmvides reliable information about tool condition. In this paper an approach for estimating R_a,and R_t 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, F_x, F_y, F_z and Vb) and two output parameters are used and the network architecture is as 7x6x6x6x2. It was found that the ANN results are very close to the experimental resuks. The developed model can be used to define the quality of surface finish in tool condition monitoring systems.