Thermomechanical Behavior of Biocompatible Austenitic Stainless Steels during Simulated Torsion Tests

Aquino, Felipe Anderson Silva de; Silva, Eden S.; Rodrigues, Samuel Filgueiras; Aranas, Clodualdo; Siciliano, Fulvio; Coutinho, Samir S.; Reis, Gedeon Silva

doi:10.1007/s11665-019-04324-4

Cited by 5 publications

(1 citation statement)

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“…Another reason for this discrepancy is the moderate stacking fault energy, γ sfe = 69 mJ/m 2 . Aquino et al [41] stated that the stacking fault energy of this steel is well above that of other austenitic steels, such as AISI 304/304L (γ sfe = 18 mJ/m 2 ) [42] and that this energy hinders the evolution of SRX/MDRX by favoring the action of thermally activated mechanisms and hence the SRV mechanism. The same finding was reported by Gerônimo et al [43], who studied AISI 316LVM steel, γ sfe = 78 mJ/m 2 [44].…”

Section: Microstructural Featuresmentioning

confidence: 90%

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

Sulzbach

Rodrigues

et al. 2022

Metals

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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.

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Section: Microstructural Featuresmentioning

confidence: 90%