Version 1
: Received: 22 January 2024 / Approved: 23 January 2024 / Online: 23 January 2024 (07:14:22 CET)
How to cite:
Mesnaoui, A.A.O.; Calderón, P.C.; Larrión, J.I.A.; Pitarch, E.P. Development of Neural Networks to Study the Behavior of a Micro-alloyed Medium Carbon Steel during Hot Forming. Preprints2024, 2024011642. https://doi.org/10.20944/preprints202401.1642.v1
Mesnaoui, A.A.O.; Calderón, P.C.; Larrión, J.I.A.; Pitarch, E.P. Development of Neural Networks to Study the Behavior of a Micro-alloyed Medium Carbon Steel during Hot Forming. Preprints 2024, 2024011642. https://doi.org/10.20944/preprints202401.1642.v1
Mesnaoui, A.A.O.; Calderón, P.C.; Larrión, J.I.A.; Pitarch, E.P. Development of Neural Networks to Study the Behavior of a Micro-alloyed Medium Carbon Steel during Hot Forming. Preprints2024, 2024011642. https://doi.org/10.20944/preprints202401.1642.v1
APA Style
Mesnaoui, A.A.O., Calderón, P.C., Larrión, J.I.A., & Pitarch, E.P. (2024). Development of Neural Networks to Study the Behavior of a Micro-alloyed Medium Carbon Steel during Hot Forming. Preprints. https://doi.org/10.20944/preprints202401.1642.v1
Chicago/Turabian Style
Mesnaoui, A.A.O., José Ignacio Alcelay Larrión and Esteban Peña Pitarch. 2024 "Development of Neural Networks to Study the Behavior of a Micro-alloyed Medium Carbon Steel during Hot Forming" Preprints. https://doi.org/10.20944/preprints202401.1642.v1
Abstract
In the present article, the application of an artificial neural network (ANN) model is studied, whose function is the development of plastic instability maps of a medium carbon microalloyed steel during the hot forging process. And, secondly, we proceed to create another neural network capable of providing the recrystallized grain size in the steady state phase resulting from the deformation by hot forging, thus creating a methodology that can be applied to different types of processes and materials.
In order to achieve this objective, we start from the experimental data of a medium carbon microalloy steel, obtained by hot compression tests, for strain rates that vary between 10-4 and 3 s-1 and in a range of temperatures between 900 oC and 1150 oC. These experimental data will be used to train the neural network that is intended to be created in this work. Once trained, it will be verified if the results of the test correspond to the experimental data and the fluency curves will be obtained. Finally, the processing maps will be developed applying the Dynamic Materials Model (DMM), according to which the safe domains to forge the material and the plastic instability domains are delineated, to be avoided during the forging process. The maps obtained by means of RNA will be compared with the experimental ones and it will be possible to verify that the optimal regions of forging in the maps obtained by RNA coincide with those obtained by means of experimental data. In addition, a study of the influence of the microstructure on the behavior of steel during hot forming will be carried out, since the experimental tests are carried out at austenitizing temperature, so the microstructure is different in each test.
Keywords
Red neuronal artificial; modelo de material dinámico; Procesamiento de mapas; Comportamiento del flujo; Acero microaleado de medio carbono
Subject
Physical Sciences, Other
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
,
*
,
*
