Neural networks are now a prominent feature of materials science with rapid progress in all sectors of the subject. It is premature, however, to claim that the method is established. There are genuine difficulties caused by the often incomplete exploration and publication of models. The assessment presented here is an attempt to compile a loose set of guidelines for maximising the impact of any models that are created, in order to encourage thoroughness in publication to a point where the work can be independently verified.

Experimental data on the tensile strength of ferritic steels designed for prolonged service at elevated temperatures have been assessed as a function of many variables, including the testing temperature. The resulting model has been combined with other data on the intrinsic strength of pure ferritic iron and substitutional solute strengthening to show that there is a regime in the temperature range 780-845 K beyond which there is a rapid decline in the microstructural contribution to strength. This decline cannot be attributed to changes in microstructure, but possibly to the ability of dislocations to overcome obstacles with the help of thermal activation. There is evidence of an approximate relationship between the temperature dependence of hot tensile strength and creep rupture stress.

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A model has been created to allow the quantitative estimation of the fatigue crack growth rate in steels as a function of mechanical properties, test-specimen characteristics, stress-intensity range and test-frequency. With this design, the remarkable result is that the method which is based on steels, can be used without modification, and without any prior fatigue test, to estimate the crack growth rates in nickel, titanium and aluminium alloys. It appears therefore that a large proportion of the differences in the fatigue crack growth rate of metallic alloys can be explained in terms of the macroscopic tensile properties of the material rather than the details of the microstructure and chemical composition.

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