caa a22 4500 463229665 CHVBK 20180405153238.0 cr unu---uuuuu 170326e20070701xx s 000 0 eng 10.1007/s10853-006-0881-2 doi (NATIONALLICENCE)springer-10.1007/s10853-006-0881-2 Artificial neural network modeling for the prediction of critical transformation temperatures in steels [Elektronische Daten] [Carlos Garcia-Mateo, Carlos Capdevila, Francisca Caballero, Carlos de Andrés] Accurate knowledge of critical transformation temperatures in steels such as the austenitizing temperature, T γ , isothermal bainite and martensite start temperatures, B S and M S , is of unquestionable significance from an industrial and research point of view. Therefore a significant amount of work has been devoted not only in understanding the physical mechanism lying beneath those transformations, but also obtaining quantitatively accurate models. Nowadays, with modern computing systems, more rigorous and complex data analysis methods can be applied whenever required. Thus, Artificial Neural Network (ANN) analysis becomes a very attractive alternative, for being easily distributed, self-sufficient and for its ability of accompanying its predictions by an indication of their reliability. Springer Science+Business Media, LLC, 2007 Garcia-Mateo Carlos MATERALIA Research Group, Department of Physical Metallurgy, Centro Nacional de Investigaciones Metalúrgicas (CENIM), Consejo Superior de Investigaciones Científicas (CSIC), Avda. Gregorio del Amo, 8, 28040, Madrid, Spain aut Capdevila Carlos MATERALIA Research Group, Department of Physical Metallurgy, Centro Nacional de Investigaciones Metalúrgicas (CENIM), Consejo Superior de Investigaciones Científicas (CSIC), Avda. Gregorio del Amo, 8, 28040, Madrid, Spain aut Caballero Francisca MATERALIA Research Group, Department of Physical Metallurgy, Centro Nacional de Investigaciones Metalúrgicas (CENIM), Consejo Superior de Investigaciones Científicas (CSIC), Avda. Gregorio del Amo, 8, 28040, Madrid, Spain aut de Andrés Carlos MATERALIA Research Group, Department of Physical Metallurgy, Centro Nacional de Investigaciones Metalúrgicas (CENIM), Consejo Superior de Investigaciones Científicas (CSIC), Avda. Gregorio del Amo, 8, 28040, Madrid, Spain aut Journal of Materials Science Kluwer Academic Publishers-Plenum Publishers; http://www.springer-ny.com 42/14(2007-07-01), 5391-5397 0022-2461 42:14<5391 2007 42 10853 https://doi.org/10.1007/s10853-006-0881-2 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s10853-006-0881-2 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Garcia-Mateo Carlos MATERALIA Research Group, Department of Physical Metallurgy, Centro Nacional de Investigaciones Metalúrgicas (CENIM), Consejo Superior de Investigaciones Científicas (CSIC), Avda. Gregorio del Amo, 8, 28040, Madrid, Spain aut NATIONALLICENCE 700 1- Capdevila Carlos MATERALIA Research Group, Department of Physical Metallurgy, Centro Nacional de Investigaciones Metalúrgicas (CENIM), Consejo Superior de Investigaciones Científicas (CSIC), Avda. Gregorio del Amo, 8, 28040, Madrid, Spain aut NATIONALLICENCE 700 1- Caballero Francisca MATERALIA Research Group, Department of Physical Metallurgy, Centro Nacional de Investigaciones Metalúrgicas (CENIM), Consejo Superior de Investigaciones Científicas (CSIC), Avda. Gregorio del Amo, 8, 28040, Madrid, Spain aut NATIONALLICENCE 700 1- de Andrés Carlos MATERALIA Research Group, Department of Physical Metallurgy, Centro Nacional de Investigaciones Metalúrgicas (CENIM), Consejo Superior de Investigaciones Científicas (CSIC), Avda. Gregorio del Amo, 8, 28040, Madrid, Spain aut NATIONALLICENCE 773 0- Journal of Materials Science Kluwer Academic Publishers-Plenum Publishers; http://www.springer-ny.com 42/14(2007-07-01), 5391-5397 0022-2461 42:14<5391 2007 42 10853 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer