Hybrid neural-fuzzy modeling for impact toughness prediction of alloy steels

As one of the most important characteristics of structural steels, toughness is assessed by the Charpy V-notch impact test. The absorbed impact energy and the transition temperature defined at a given Charpy energy level are regarded as the common criteria for toughness assessment. This paper aims at establishing generic toughness prediction models which link materials compositions and processing conditions with Charpy impact properties. Hybrid knowledge-based neural-fuzzy modeling techniques which incorporate linguistic knowledge into data-driven neural-fuzzy models have been used to develop the Charpy properties prediction models for thermomechanically controlled rolled (TMCR) steels. Two basic ways of knowledge incorporation are introduced to improve the performance of the obtained fuzzy models. Simulation experiments show that both numeric data and linguistic information can be combined in a unified framework and that both Charpy impact energy and the impact transition temperature (ITT) can be predicted by the same model