Abstract
Present work of this paper focus on developing a milling force model according to the characteristic of the matching of lengthened shrink-fit holder (LSFH) and cutter using back propagation neural network (BPNN). Time parameter is taken as a factor of the input vector besides 6 processing conditions which mainly affect the milling force, and then the forecasting of 3D transient milling forces are achieved. A lot of milling experiments were performed to get training and testing samples and a Matlab program was designed to evaluate and optimize the network. The test experiments show that the forecasting results are well agreed with the experimental results and the errors of 3D force components are less than 0.18. Besides an extended performance, the BPNN model has higher efficiency and higher accuracy than the customary analytical model.
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