| In the process of machining surface , Errors of rough in dimension, shape and location lead to the change in processing quantity. The material of work piece isn't equal. For all these reasons cutting force changes in machining the machining system deform. Consequently errors of work piece come into being. This is the so-called error reflection phenomenon. Generally such errors are reduced through many times of processing and adopting appropriate processing quantity each time according the operator's experience.According to the theory of error reflection the error reflection coefficient indicates to which extent errors of rough influence errors of work piece. It is connected with several factors such as machining condition,hardness of work piece and so on. Such non-linear relation can not be worked out by formula,While BP network can simulate such non-linear relation for its randomnon-linear mapping ability. BP arithmetic is not limited by the input numbers and output numbers. In the actual research,you can modify the program freely as you need.In this research the main goal is to solve error reflection phenomenon in surface milling and to select optimum machining parameters. The final purpose is to obtain the better and distribute-uniformly quality of the surface Through analyzing the model of error reflection and comparing different kinds of neural networks the model of neural network are confirmed. The essential of solving error refection phenomenon in milling is to approximate a complex non-linear function. While perception can only solve sorting problem whose input can be linearly sorted, Adaptive linear element can only learn linear relation between input and output. BP network can approximate such complex non-linear function for its strong non-linear mapping ability. Through analysis of error refections model, Error of rough (â–³1),error of work piece after milling(â–³0) , rigidity of milling system(KS), hardness... |
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