Artificial neural networks for modelling the mechanical properties of steels in various applications

The sensitivity, which is a measure of the response of an output across the range of an individual input variable, of key input variables (individual alloys and/or process steps) for each model is shown to be in agreement with findings of both the experimental investigation and reports in the literature. Although this paper shows that ANNs can be employed for optimizing steel and process design parameters, some difficulty can arise when inter-relationships exist between input variables. An understanding of the inter-relationships between input variables is essential for interpreting the sensitivity data and optimizing design parameters. It is argued that artificial neural network models can be developed that have the capacity to eliminate the need for expensive experimental investigation in areas, such as welding (new and repair), inspection and testing, and manufacturing processes.