Abstract
Over the last five years efforts have been devoted towards the development and validation of mechanical test result models relating to a range of alloy steels. Several neural-network based models have been developed, two of which are related to the mechanical test results of Ultimate Tensile Strength (UTS) and Reduction of Area (ROA). The ultimate aim of developing these models is to pave the way to process optimisation through better predictions of mechanical properties. In this research the exploitation of such neural network models is proposed in order to determine the optimal alloy composition and heat treatment temperatures required, given certain predefined mechanical properties such as the UTS and ROA. Genetic Algorithms are used for this purpose. The results obtained are very encouraging.
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Mahfouf, M. (2002). Optimal Design of Alloy Steels Using Genetic Algorithms. In: Zimmermann, HJ., Tselentis, G., van Someren, M., Dounias, G. (eds) Advances in Computational Intelligence and Learning. International Series in Intelligent Technologies, vol 18. Springer, Dordrecht. https://doi.org/10.1007/978-94-010-0324-7_30
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DOI: https://doi.org/10.1007/978-94-010-0324-7_30
Publisher Name: Springer, Dordrecht
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