Conclusions

1. It has been possible to design a neural network model for fatigue crack growth in steels, which relies on inputs that consist only of properties that can be obtained from a simple tensile test, and using information about specimen geometry and testing parameters.

2. Given the nature of the inputs, it has been demonstrated that although the model is based entirely on data from steels, it can be applied without modification to nickel, titanium and aluminium alloys.

3. Given recent work where a similar approach has been used in modelling the hot-tensile strength [40] and stretch flangeability [41], it becomes evident that the neural network method has enormous potential for creating models for complex mechanical properties on the basis of simple experiments, such as the data obtained during tensile testing.

The computer program associated with this work can be downloaded freely from:

$$\hbox{https://www.phase-trans.msm.cam.ac.uk/map/mapmain.html}$$