Abstract
The interface friction is used to evaluate the plastic flow behavior of workpieces material in different conditions such as temperature, pressure, strain rate and strain distribution. It is analyzed that deformation and material flow by the ring compression method during compression, which is contained with the theoretical calculation of different interface friction ring inside diameter and the height of the change. The synthetical symptoms of incremental method is concluded. Using wavelet and mixed data merge does the intelligence diagnosis to the defect of incremental method, which is integrated with data , characteristic, decision grate and nerve network. A model of wavelet neural network is constructed. In order to reduce defect analysis , the excellent diagnosis way is studied with the information of many sources fill and redundant. The result is given that using mixed data merge may raise tolerate character with the help of many sources fill and redundant, and using wavelet and mixed data merge does the effective diagnosis of ceramic friction and flow stress. Keywords-wavelet neural; network; defect; incremental method; friction factor
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