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

• Published in: Journal of materials processing technology Vol. 170; no. 3; pp. 536 - 544
• Main Authors: Sterjovski, Z., Nolan, D., Carpenter, K.R., Dunne, D.P., Norrish, J.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 30.12.2005
• Subjects: Artificial neural network modelling; Continuous casting; Hardness; Hot ductility; Impact toughness; In-service welding; Steels; Artificial neural network modelling; Continuous casting; In-service welding; Impact toughness; Steels; Hardness; Hot ductility
• ISSN: 0924-0136
• DOI: 10.1016/j.jmatprotec.2005.05.040

The application of artificial neural networks (ANNs) in predicting some key properties of steels is discussed in detail. This paper reports on the effectiveness of three back-propagation artificial neural network models that predict (i) the impact toughness of quenched and tempered pressure vessel steel exposed to multiple postweld heat treatment (PWHT) cycles, (ii) the hardness of the simulated heat affected zone in pipeline and tap fitting steels after in-service welding and (iii) the hot ductility and hot strength of various microalloyed steels over the temperature range for strand or slab straightening in the continuous casting process. Predicted and actual experimental values for each model are well matched and highlight the success of applying ANNs in predicting mechanical properties. The capability of ANNs in predicting multiple outputs (hot ductility and hot strength) is also demonstrated. 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.