Experimantal numeral study of temperature distribution during milling process in A537CL2 steel artivicial neural network alloy using

Experimental amp; numeral study of temperature distribution during milling process, is important in milling quality and tools life aspects .In the present study the milling cross-section temperature is determined by using Artificial Neural Networks ( ANN ) according to the temperature of certain points of the work piece and the points specificallons and the milling rotational speed of the blade. In the present work, at first three-dimensional model of the work piece is provided and then by using the Computational Heat Transfer ( CHT ) simulations, temperature in different nods of the work piece are specified in steady-state conditions. Results obtained from CHT are used for training and testing the ANN approach. Using reverse engineering and setting the desired x , y , z and the milling rotational speed of the blade as input data to the network , the milling surface temperature determined by neural network is presented as output data . the desired points temperature for different milling blade rotational speed are obtained experimentally and by extrapolation method for the milling surface temperature is obtained and a comparison is performed among the soft programming ANN , CHT results and experimental data and it is observed that ANN soft programming code can be used more efficiently to determine the temperature in a milling process