Application of the backpropagation neural network method in designing tungsten heavy alloy

The model describing the dependence of the mechanical properties on the chemical composition and as deformation techniques of tungsten heavy alloy is established by the method of improved the backpropagation neural network. The mechanical properties' parameters of tungsten alloy and deformation techniques for tungsten alloy are used as the inputs. The chemical composition and deformation amount of tungsten alloy are used as the outputs. Then they are used for training the neural network. At the same time, the optimal number of the hidden neurons is obtained through the experiential equations, and the varied step learning method is adopted to ensure the stability of the training process. According to the requirements for mechanical properties, the chemical composition and the deformation condition for tungsten heavy alloy can be designed by this artificial neural network system.