Modeling Pit Growth as a Function of Environmental Variables Through Stochastic Approaches Available to Purchase

It is well known that rates of pitting corrosion damage are influenced by environmental variables such as applied potential, temperature, and concentrations of ions in solution. Regression methods have often been used to estimate the maximum pit depth as a function of environmental parameters. In this paper, regression methods are used both to predict the maximum depth and to estimate the probability distribution of the maximum depth at a future time. It is shown that these predictions can be reasonably accurate (within 15% of the true value) even when the true mathematical relationships between pitting rates and environmental parameters are nonlinear, and in some circumstances when future values of the environmental variables are not known.