Mathematical modeling of residual stresses and distortions induced by gas nitriding of 32CrMoV13 steel

Depouhon, P.; Sprauel, Jean‐Michel; Mailhé, M.; Mermoz, Emmanuel

doi:10.1016/j.commatsci.2013.09.043

Cited by 14 publications

(12 citation statements)

References 32 publications

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“…[19]) for constant activity in the gas phase (i.e., constant nitriding potential). This effect is only considered for incorporating mobile excess nitrogen due to the significant effect of the mobile excess nitrogen on the overall nitriding kinetics.…”

Section: A Numerical Methodsmentioning

confidence: 99%

“…[5]) is updated by Eqs. [19] through [22]. The total N concentration at depth step k, (C N tot ) k j , is calculated by:…”

Section: A Numerical Methodsmentioning

confidence: 99%

“…[25,26] In some investigations, this effect was taken into account, [10,12,16,18] but it has been disregarded in many other studies, [11,[13][14][15]17,19,20] where instantaneous establishment of a (equilibrium) dissolved N concentration at the surface was assumed.…”

Section: Introductionmentioning

confidence: 99%

“…The model was generalized by considering a finite solubility of I, leading to some amount of dissolved I ahead of the reaction front during the precipitation; [6] in this variant, the model has been widely applied to carburizing [7][8][9] and nitriding. [10][11][12][13][14][15][16][17][18][19] A serious shortcoming of the type of models introduced earlier [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19] is the disregard of the kinetics (the time and temperature dependencies) of the precipitation reaction. Moreover, these models describe the evolution of only the total amount of precipitated M y I z and cannot predict number density and size (distribution) of the precipitates.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, it would be highly beneficial to have at one's disposal a model that links the inward diffusion of I with the (depth-and time-dependent) precipitation state (volume fraction, number density, and size distribution) of the M y I z particles so that the microstructure of the alloys can be predicted and optimized. [1] Until now a series of attempts have been made to simulate inward diffusion of I and simultaneous precipitation of M y I z , [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20] usually focusing on one specific (extreme) case of inward diffusion and precipitation. A simple model of coupled diffusion and precipitation was thus developed for the case of internal oxidation [2,3] and later applied to nitriding [4] and carburizing.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Coupling Inward Diffusion and Precipitation Kinetics; the Case of Nitriding Iron-Based Alloys

Jung

Meka

Rheingans

et al. 2016

Metall Mater Trans A

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A model that describes the inward diffusion of an element I into a solid substrate and the simultaneous precipitation of a compound M y I z , with M as the alloying element initially dissolved in the substrate matrix, is presented for the case of nitriding iron-based alloys. The model was developed by coupling the diffusion kinetics and the precipitation (nucleation and growth) kinetics. Additionally, the role of excess nitrogen and the kinetics of ammonia dissociation at the iron surface were incorporated into this coupled model. The model was successfully applied to the case of nitriding an Fe-2.23 at. pct V alloy; the simulation results are in good agreement with the measured data and allow for detailed understanding of the evolution of the nitride precipitates (volume fraction, number density, and size distribution) as a function of both nitriding time and depth in the specimen. The present model exposed the pronounced effects of the precipitation kinetics, of excess nitrogen, and of the surface-reaction kinetics on the overall nitriding kinetics and demonstrated a striking, nonmonotonous change with time of precipitate particle size at a distinct depth in the specimen.

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Section: A Numerical Methodsmentioning

confidence: 99%

“…[5]) is updated by Eqs. [19] through [22]. The total N concentration at depth step k, (C N tot ) k j , is calculated by:…”

Section: A Numerical Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Coupling Inward Diffusion and Precipitation Kinetics; the Case of Nitriding Iron-Based Alloys

Jung

Meka

Rheingans

et al. 2016

Metall Mater Trans A

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Employing Deep Neural Networks and High‐Throughput Computing for the Recognition and Prediction of Vein‐Like Structures

Niu,

Chi,

Wang

et al. 2024

Advanced Intelligent Systems

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In this investigation, convolutional neural networks (CNNs) are leveraged to engineer a simple segmentation and recognition algorithm specialized for the delineation of complex, network‐like morphologies—often termed “vein‐like structures (VLSs)”—in scanning electron microscopy (SEM) imagery. These intricate formations frequently appear during the nitriding treatment of medium‐ to high‐carbon alloy steels. To navigate the multifaceted characteristics of such architectures, CNN‐based methodologies are synergized with high‐throughput thermodynamic computations via Thermo‐Calc. This integration aims to quantify both the theoretical upper bounds and the actual values of the VLSs. By establishing deep neural network models for both theoretical upper bounds and actual measurements, the gap between thermodynamics and thermokinetics in the nitriding process is bridged. Applying this amalgamated predictive schema to 8Cr4Mo4V steel, a groundbreaking departure from conventional paradigms that exclusively depend on thermodynamic calculation‐based diffusion models is effectuated. The emergent model yields transformative implications for the metallurgical sector, paving the way for the refinement of future nitriding algorithms and enhancements in nitriding methodologies.

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Improvement of the Predictive Ability of Polycyclic Fatigue Criteria for 42CrMo4 Nitrided Steels

Bechouel

Moussa

Terres

2019

Advances in Mechanical Engineering and Mechanics

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Mathematical modeling of residual stresses and distortions induced by gas nitriding of 32CrMoV13 steel

Cited by 14 publications

References 32 publications

Coupling Inward Diffusion and Precipitation Kinetics; the Case of Nitriding Iron-Based Alloys

Coupling Inward Diffusion and Precipitation Kinetics; the Case of Nitriding Iron-Based Alloys

Employing Deep Neural Networks and High‐Throughput Computing for the Recognition and Prediction of Vein‐Like Structures

Improvement of the Predictive Ability of Polycyclic Fatigue Criteria for 42CrMo4 Nitrided Steels

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