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

• Published in: Metals (Basel ) Vol. 12; no. 11; p. 1783
• Main Authors: Sulzbach, Gláucia Adriane de S., Rodrigues, Maria Verônica G., Rodrigues, Samuel F., Lima, Marcos Natan da S., Loureiro, Rodrigo de C. Paes, Sá, Denis Fabrício S. de, Aranas, Clodualdo, Macedo, Glaucia Maria E., Siciliano, Fulvio, Abreu, Hamilton F. Gomes de, Reis, Gedeon S., Silva, Eden S.
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.11.2022
• Subjects: Alloys; artificial neural network (ANN); Artificial neural networks; austenitic stainless steel; Austenitic stainless steels; Biological products; Biomedical materials; Compression tests; Constitutive models; Deformation; Extensometers; Forging; Grain boundaries; Grain size; Hot pressing; Hot rolling; Investigations; Kinetics; Manufacturing; Mechanical properties; metadynamic recrystallization; Microscopy; Neural networks; Optimization; Parameter identification; Physical simulation; precipitation; Recrystallization; Simulation; Softening; Stacking fault energy; Steel; Strain rate; thermomechanical processing; Thermomechanical properties; Thermomechanical treatment; Vacuum chambers; Brazil; Canada
• ISSN: 2075-4701; 2075-4701
• DOI: 10.3390/met12111783

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.