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A new approach based on BP neural network integrated with genetic algorithm for fitting of microdrill's margin projection is presented. The network is structured according to fitting equations, where sampled point coordinates of micro-drill, their recombination and constant 1 are taken
as 6 inputs, and 1 output is obtained. The square of error between the output and constant 0 is taken as performance index; and the weights between the input neurons and output neuron are tuned in the light of gradient descent method, and stable weight values are obtained while the desired
performance index is reached. In order to obtain global optimal solution, genetic algorithm is integrated into the fitting program, and expression coefficients of micro-drill's margin projection are solved according to the neural network's weights; thus the radiuses of rounded corners and
diameter of the micro-drill are tested easily. The proposed approach has advantages of programming easily and higher precision over conventional approaches such as least square method and so on.
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