Published [25] fatigue crack growth data for tests done in ordinary air, at room temperature, were digitised, covering steels with chemical compositions in the range presented in Table 1. Traces element concentrations (Ti, Al, V, S, P) together with the details of heat treatment can be found in the original compilation [25]. The properties of a steel depend on the composition and heat treatment, but fatigue crack propagation should depend to a large extent on macroscopic mechanical properties. It was deliberately decided to focus on easily measured properties obtained from a tensile test, rather than use inputs such as the threshold stress intensity which would defeat the purpose of modelling since a fatigue test would be required before a prediction could be made. The dimensions of the test specimens and the test conditions are also important in this respect and were included in the analysis. The advantage of this approach also is that a large quantity of data are available with each of the input variables listed in Table 1. Data for both axial mode I and an in-plane bending mode II were incorporated; mode III data were not available.
The plots in Fig. 1 illustrate the distribution of data, but clearly cannot represent multidimensional dependencies. However, the neural network method used here is based on a Bayesian framework [15,13] so that the predictions are associated with a modelling uncertainty whose magnitude depends on the position in the input domain where a calculation is done. . As pointed out previously, the details of the neural network and Bayesian framework used have been fully described elsewhere so only the essential points are included in this paper.