Composition-dependence of stacking fault energy in austenitic stainless steels through linear regression with random intercepts

Bellefon, G. Meric de; Duysen, J.C. Van; Sridharan, Kumar

doi:10.1016/j.jnucmat.2017.05.037

Cited by 109 publications

(38 citation statements)

References 24 publications

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“…It is possible to infer that the increase in hardness observed on the surface of the as-machined and laser-heated specimen would be even higher, because edge effects caused by the vicinity of the boundary between material and resin would result in measuring apparently lower values of hardness. Even taking into account the measured large increase in surface hardness, it was considered unlikely that strain-induced phase transformation processes would happen in this alloy, as its austenitic structure is stabilized by Ni, and, most importantly the 2-3% of Mo increases the stacking fault energy [42,43]. Measurable hardening effects caused by laser heating appeared not to extend below the 50 µm deep layer and are consistent with published work in the literature [44].…”

Section: Discussionsupporting

confidence: 78%

Laser Irradiation Effects in Simulated Laser-Assisted Machining of Type 316L SS

Maurotto

Scenini

Kraemer

2018

JMMP

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Laser surface heating allows for the thermal treating of clearly defined surface areas thanks to the ability to focus the laser beam to a specific point. Thus, the rapid heating and subsequent rapid cooling when the beam is moved away, typically associated with laser light, is used as an in-machine process to improve the machinability of hard- or difficult-to-machine alloys. In laser-assisted machining (LAM), laser irradiation occurs simultaneously with materials removal; however, it is difficult to ensure a complete removal of the irradiated areas. In the present work, the two processes were decoupled to investigate the interaction effects of laser radiation type 316L. The surface residual stress, hardness, and microstructure of milled flat specimens were measured prior to and after diode-generated laser beam irradiation. Laser exposure of samples was conducted under protective gas shielding (Argon) using heating parameter combinations that would limit or avoid laser surface melting. Conversely, when the surface underwent melting, the formation of a fast solidification layer resulted in the removal of the cold-worked effect and the significant softening of the surface layers. Beam power density in-homogeneities and incomplete machining of the treated areas in LAM have the potential to introduce significant undesired changes on components’ surface integrity.

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Section: Discussionsupporting

confidence: 78%

Laser Irradiation Effects in Simulated Laser-Assisted Machining of Type 316L SS

Maurotto

Scenini

Kraemer

2018

JMMP

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“…www.erpublication.org Employing the same selected characterized chemical composition data base from Yonezawa´s work [1] as mentioned previously, SFE values were evaluated. The later was performed using the models reported by [1], [14], [24]- [26] and the proposed ANN model from this work. It is important to point out that these calculations correspond only for the austenitic SS in the water cooling condition (SHTWC).…”

Section: Resultsmentioning

confidence: 99%

“…On the other hand, the sample characterization and preparation techniques must be considered to control the dispersion among the difference in SFE calculated. It has not been reported a specific influence of this process parameters in the simulation models developed [12], [24]- [26]. Thus, we propose the design of ANN model based on chemical composition, heat treatment and the TEM as a characterization technique, the latter due to its accuracy in obtaining the dislocation mechanism for the austenitic SS.…”

Section: Discussionmentioning

confidence: 99%

“…In the last 60 years, several works have been carried out with different techniques to determinate the SFE, among them are characterization by transmission electron microscopy (TEM) [1], [11]- [17]; X-ray diffraction (XRD) [18][19][20][21]; neutron diffraction [16], [22]. In the other hand, it has been developed expressions by linear multivariables analysis of experimental data [1], [12], [14], [23]- [26], and also computational thermodynamics and quantum mechanics first-principles simulations [1], [17], [27]- [31]. Recently, Arpan Das [32] developed a model of a Bayesian neural network for the calculation of SFE.…”

Section: Introductionmentioning

confidence: 99%

“…The ANN was specifically designed and constrained for 33 Mo content austenitic SS alloys, considering only the SFE data reported for the three heat treatments [1]. Also the results were compared with other authors [1], [14], [24]- [26].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Prediction of the stacking fault energy in austenitic stainless steels using an artificial neural network

Román¹,

Campillo²,

Sarmiento³

et al. 2019

IJETR

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Stacking fault energy (SFE) is an important parameter to be considered in the design of austenitic stainless steels (SS) due to its influence on magnetic susceptibility, atomic order changes and intergranular corrosion resistance. An extensive review of specialized literature was examined in order to understand the different methods that have been developed for the calculation of SFE. Characterization by transmission electron microscopy (TEM), linear expressions from data processing and first-principles quantum mechanics approximations are some techniques that have been used for this objective. In the present work a feed forward backpropagation artificial neural network (ANN) was developed to predict the SFE within given specific ranges of chemical compositions of austenitic SS. The experimental data were extracted and analyzed from a research work reported by Yonezawa et al [1], for three different heat treatment conditions. The model predicts SFE values with a correlation coefficient of 0.99, which reduce the error when is compared with other works in the literature. Index Terms-Neural network, Stacking fault energy, Austenitic stainless steel.

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Grain Boundary Sliding During High Pressure Torsion of Nanocrystalline Au‐13Pd Alloy

Skrotzki,

Pukenas,

Jóni

et al. 2024

Adv Eng Mater

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The microstructure and texture were investigated for nanocrystalline Au‐13at.%Pd deformed by high‐pressure torsion. The grain size of this alloy is observed to remain below about 20 nm when subjected to severe plastic deformation. Surprisingly, the initial <110> powder compaction texture did not change significantly during shearing. The results are explained in terms of a grain boundary sliding mechanism involving planar interfaces formed by grain boundary migration.This article is protected by copyright. All rights reserved.

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Composition-dependence of stacking fault energy in austenitic stainless steels through linear regression with random intercepts

Cited by 109 publications

References 24 publications

Laser Irradiation Effects in Simulated Laser-Assisted Machining of Type 316L SS

Laser Irradiation Effects in Simulated Laser-Assisted Machining of Type 316L SS

Prediction of the stacking fault energy in austenitic stainless steels using an artificial neural network

Grain Boundary Sliding During High Pressure Torsion of Nanocrystalline Au‐13Pd Alloy

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