Abstract:

A prediction model for corrosion and fatigue performances of the prior corroded aluminum alloys under a varied corrosion environmental spectrum based on artificial neural net was developed and the non linear relationship between maximum corrosion depth,fatigue performance and corrosion temperature,time was established based on BP learning algorithm analysis and convergence improvement.The maximum corrosion depth and fatigue performances of prior corroded aluminum alloys can be predicted by means of the trained neural net from the testing data. The learning algorithm for neural net is BP(back\|propagation) algorithm with 2 4 2 structure.The results show that,for multi\|factor corrosion prediction,the prediction model based on BP learning algorithm for corrosion and fatigue performances of the prior corroded aluminum alloys is feasible and effective.Thus,by virtue of the prediction model,the future corrosion status and fatigue performances of aluminum alloys can be evaluated under random complicated environmental spectrum.

Key words: prior corroded, aluminum alloys, neural net, fatigue, detail fatigue rating, environmental spectrum