Principal Investigator:

The development of new nickel alloys for aeroengine applications is a difficult taks, frequently achieved by trial and experience. The purpose of this work was to enable a significant proportion of the development procedure to be done by computation. A variety of methods have been used towards this end, ranging from physical models to the power technique of neural network analysis.

A more general method of regression is neural network
analysis. As before, the input data x_{j} are multiplied by weights,
but the sum of all these products forms the argument of a hyperbolic
tangent. The output y is therefore a non-linear function of
x_{j}, the function usually chosen being the hyperbolic tangent
because of its flexibility. The exact shape of the hyperbolic
tangent can be varied by altering the weights. Further
degrees of non-linearity can be introduced by combining several of
these hyperbolic tangents, so that the neural network
method is able to capture almost arbitrarily non-linear
relationships. For example, it is well known that the effect of
chromium on the microstructure of steels is quite different at large
concentrations than in dilute alloys. Ordinary regression analysis
cannot cope with such changes in the form of relationships.

A potential difficulty with the use of powerful regression methods is
the possibility of overfitting data. For example, it is
possible to produce a neural network model for a
completely random set of data. To avoid this difficulty, the
experimental data can be divided into two sets, a * training*
dataset and a * test* dataset. The model is produced using only the
training data. The test data are then used to check that the
model behaves itself when presented with previously unseen data.

Neural network models in many ways mimic human experience and are capable of learning or being trained to recognize the correct science rather than nonsensical trends. Unlike human experience, these models can be transferred readily between generations and steadily developed to make design tools of lasting value. These models also impose a discipline on the digital storage of valuable experimental data, which may otherwise be lost with the passage of time.

We have addressed solid solution strengthening, tensile properties, fatigue, creep, lattice misfit in the context of nickel-base superalloys, and have applied the method to other materials and processes.

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The work was supported by the Engineering and Physical Sciences Research Council (EPSRC) of the United Kingdom. The results of the project have been assessed by EPSRC referees as follows:

- Scientific & Technological Merit: Very significant contribution to the field.
- Management and use of resources: Good