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Article

Optimization of Thermomechanical Processing under Double-Pass Hot Compression Tests of a High Nb and N-Bearing Austenitic Stainless-Steel Biomaterial Using Artificial Neural Networks

by
Gláucia Adriane de S. Sulzbach
1,
Maria Verônica G. Rodrigues
1,
Samuel F. Rodrigues
1,2,*,
Marcos Natan da S. Lima
3,
Rodrigo de C. Paes Loureiro
3,
Denis Fabrício S. de Sá
4,
Clodualdo Aranas, Jr.
5,
Glaucia Maria E. Macedo
6,
Fulvio Siciliano
1,7,
Hamilton F. Gomes de Abreu
3,
Gedeon S. Reis
1 and
Eden S. Silva
1,6
1
Graduate Program in Materials Engineering, Federal Institute of Education, Science and Technology of Maranhão-IFMA, São Luís 65075-441, Maranhão, Brazil
2
Materials Science and Engineering Graduate Program, Federal University of Piaui—UFPI, Teresina 64049-550, Piaui, Brazil
3
Materials Characterization Laboratory (LACAM), Department of Metallurgical and Materials Engineering, Federal University of Ceará, Campus Do Pici, Bloco 729, Fortaleza 60020-181, Ceará, Brazil
4
Electric Engineering Department-Balsas (COELE), Federal University of Maranhão—UFMA, Campus Balsas, MA-140, KM 04, Balsas 65800-000, Maranhão, Brazil
5
Mechanical Engineering, University of New Brunswick, Fredericton, NB E3B 5A3, Canada
6
Federal Institute of Education, Science and Technology of Maranhão-IFMA, Campus Barra do Corda, Barra do Corda 65950-000, Maranhão, Brazil
7
Dynamic Systems Inc., 323 NY 355, Poestenkill, NY 12140, USA
*
Author to whom correspondence should be addressed.
Metals 2022, 12(11), 1783; https://doi.org/10.3390/met12111783
Submission received: 21 September 2022 / Revised: 17 October 2022 / Accepted: 18 October 2022 / Published: 23 October 2022
(This article belongs to the Special Issue Application of Neural Networks in Processing of Metallic Materials)
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Abstract

Physical simulation is a useful tool for examining the events that occur during the multiple stages of thermomechanical processing, since it requires no industrial equipment. Instead, it involves hot deformation testing in the laboratory, similar to industrial-scale processes, such as controlled hot rolling and forging, but under different conditions of friction and heat transfer. Our purpose in this work was to develop an artificial neural network (ANN) to optimize the thermomechanical behavior of stainless-steel biomaterial in a double-pass hot compression test, adapted to the Arrhenius–Avrami constitutive model. The method consists of calculating the static softening fraction (Xs) and mean recrystallized grain size (ds), implementing an ANN based on data obtained from hot compression tests, using a vacuum chamber in a DIL 805A/D quenching dilatometer at temperatures of 1000, 1050, 1100 and 1200 °C, in passes (ε1 = ε2) of 0.15 and 0.30, a strain rate of 1.0 s−1 and time between passes (tp) of 1, 10, 100, 400, 800 and 1000 s. The constitutive analysis and the experimental and ANN-simulated results were in good agreement, indicating that ASTM F-1586 austenitic stainless steel used as a biomaterial undergoes up to Xs = 40% of softening due solely to static recovery (SRV) in less than 1.0 s interval between passes (tp), followed by metadynamic recrystallization (MDRX) at strains greater than 0.30. At T > 1050 °C, the behavior of the softening curves Xs vs. tp showed the formation of plateaus for long times between passes (tp), delaying the softening kinetics and modifying the profile of the curves produced by the moderate stacking fault energy, γsfe = 69 mJ/m2 and the strain-induced interaction between recrystallization and precipitation (Z-phase). Thus, the use of this ANN allows one to optimize the ideal thermomechanical parameters for distribution and refinement of grains with better mechanical properties.
Keywords: physical simulation; thermomechanical processing; austenitic stainless steel; metadynamic recrystallization; precipitation; artificial neural network (ANN) physical simulation; thermomechanical processing; austenitic stainless steel; metadynamic recrystallization; precipitation; artificial neural network (ANN)

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MDPI and ACS Style

Sulzbach, G.A.d.S.; Rodrigues, M.V.G.; Rodrigues, S.F.; Lima, M.N.d.S.; Loureiro, R.d.C.P.; Sá, D.F.S.d.; Aranas, C., Jr.; Macedo, G.M.E.; Siciliano, F.; Abreu, H.F.G.d.; et al. Optimization of Thermomechanical Processing under Double-Pass Hot Compression Tests of a High Nb and N-Bearing Austenitic Stainless-Steel Biomaterial Using Artificial Neural Networks. Metals 2022, 12, 1783. https://doi.org/10.3390/met12111783

AMA Style

Sulzbach GAdS, Rodrigues MVG, Rodrigues SF, Lima MNdS, Loureiro RdCP, Sá DFSd, Aranas C Jr., Macedo GME, Siciliano F, Abreu HFGd, et al. Optimization of Thermomechanical Processing under Double-Pass Hot Compression Tests of a High Nb and N-Bearing Austenitic Stainless-Steel Biomaterial Using Artificial Neural Networks. Metals. 2022; 12(11):1783. https://doi.org/10.3390/met12111783

Chicago/Turabian Style

Sulzbach, Gláucia Adriane de S., Maria Verônica G. Rodrigues, Samuel F. Rodrigues, Marcos Natan da S. Lima, Rodrigo de C. Paes Loureiro, Denis Fabrício S. de Sá, Clodualdo Aranas, Jr., Glaucia Maria E. Macedo, Fulvio Siciliano, Hamilton F. Gomes de Abreu, and et al. 2022. "Optimization of Thermomechanical Processing under Double-Pass Hot Compression Tests of a High Nb and N-Bearing Austenitic Stainless-Steel Biomaterial Using Artificial Neural Networks" Metals 12, no. 11: 1783. https://doi.org/10.3390/met12111783

APA Style

Sulzbach, G. A. d. S., Rodrigues, M. V. G., Rodrigues, S. F., Lima, M. N. d. S., Loureiro, R. d. C. P., Sá, D. F. S. d., Aranas, C., Jr., Macedo, G. M. E., Siciliano, F., Abreu, H. F. G. d., Reis, G. S., & Silva, E. S. (2022). Optimization of Thermomechanical Processing under Double-Pass Hot Compression Tests of a High Nb and N-Bearing Austenitic Stainless-Steel Biomaterial Using Artificial Neural Networks. Metals, 12(11), 1783. https://doi.org/10.3390/met12111783

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