Neural network analysis of the influence of chemical composition on surface cracking during hot rolling of AISI D2 tool steel
作者： M. Terčelj , R. Turk , G. Kugler , I. Peruš
DOI: 10.1016/J.COMMATSCI.2007.09.009
关键词: Cracking 、 Metallurgy 、 Chemical composition 、 Tin 、 Tool steel 、 Aluminium 、 Microstructure 、 Manganese 、 Materials science 、 Copper 、 General Physics and Astronomy 、 General Materials Science 、 General Computer Science 、 Mechanics of Materials 、 General chemistry 、 Computational mathematics
摘要: Abstract The reasons for the formation of surface cracks during hot rolling tool steels are not well understood. However, we know that apart from parameters thermo-mechanical processing, chemical composition steel has a big influence on these cracks. majority examinations deformability (appearance cracks) various grades made so far were limited to studying minor number elements cracks, where databases based laboratory tests. This paper proposes new approach study workability by analysing crack AISI D2 steel. database was formed results cracking stock in an industrial process and stock’s composition. analysis spatial performed with CAE neural networks, included carbon carbide-forming elements, manganese sulphur, copper, tin, aluminium, etc. analyses revealed understanding influences, thus also possibility reduce amount if concentrations be closer exactly determined values, or more ratios.
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