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The boronizing kinetics of AISI 316L steel has been analyzed by employing five prediction models. The boron diffusion coefficients as well as the growth rate constants in the FeB and Fe2B phases were firstly evaluated in the range of 1123-1223 K. Afterwards, the values of boron activation energies in FeB and Fe2B were secondly deduced by adopting the Arrhenius relationships.In addition, the prediction models have been validated experimentally for two boronizing conditions (1170 K for 1.6 h and 1210 K for 1.1 h). The predicted results were deemed very concordant with the experiments. Furthermore, advantages and limitations about the applicability of these models were also discussed.
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The boronizing kinetics of AISI 316L steel has been analyzed by employing five prediction models. The boron diffusion coefficients as well as the growth rate constants in the FeB and Fe2B phases were firstly evaluated in the range of 1123-1223 K. Afterwards, the values of boron activation energies in FeB and Fe2B were secondly deduced by adopting the Arrhenius relationships.In addition, the prediction models have been validated experimentally for two boronizing conditions (1170 K for 1.6 h and 1210 K for 1.1 h). The predicted results were deemed very concordant with the experiments. Furthermore, advantages and limitations about the applicability of these models were also discussed.

Prediction models for the kinetics of iron boride layers on AISI 316L steel

Laboratoire de Technologie des Matériaux

KOM – Corrosion and Material Protection Journal

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Prediction models for the kinetics of iron boride layers on AISI 316L steel"}

The boronizing kinetics of AISI 316L steel has been analyzed by employing five prediction models. The boron diffusion coefficients as well as the growth rate...

Prediction models for the kinetics of iron boride layers on AISI 316L steel

Pubblicato online: 18 apr 2022

Pagine: 40 - 49

DOI: https://doi.org/10.2478/kom-2022-0007

© 2022 S. Mansour et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.