Prediction of pit-related damage accumulation in aluminum alloys using stochastic modeling

A physically-based phenomenological Monte Carlo algorithm has been developed to model pitting initiation and pitting growth processes in aluminum alloys. Pitting initiation and pitting growth are considered as a series of discrete events whose occurrence is influenced by environmental factors such as [Cl^-], temperature or pH. The influence of microstructure was also taken into account. Experimental input parameters such as pit birth and death rates, and also pit growth rates obtained on 7075-T6 alloys were used. Pit depth distribution and pitted morphologies were compared with experimental data. Agreement and validation of the model are necessary to use the designed model as an actual predictive tool.