A physics-informed neural network-based numerical inverse method for optimization of diffusion coefficients in NiCoFeCr multi principal element alloy

Kumar, H. and Dash, A. and Bhattacharya, Saswata and et al, . (2022) A physics-informed neural network-based numerical inverse method for optimization of diffusion coefficients in NiCoFeCr multi principal element alloy. Scripta Materialia, 214. pp. 1-5. ISSN 1359-6462

[img] Text
1-s2.0-S1359646222001397-main.pdf - Published Version
Restricted to Registered users only

Download (1MB) | Request a copy

Abstract

The composition-dependent pseudo-binary (PB) interdiffusion coefficients and the main intrinsic diffusion coefficients of all the components at the near equiatomic composition of NiCoFeCr system are estimated following the PB diffusion couple method. These are otherwise impossible to estimate directly following the conventional method. Subsequently, a physics-informed machine learning based numerical inverse method is used to optimize the diffusion parameters in two steps. Initially, optimization is done by developing a good match with the diffusion profiles and estimated interdiffusion coefficients over whole composition range of the diffusion couples. However, a mismatch was found in the extracted intrinsic diffusion coefficients. Therefore, the second level of optimization is done with estimated intrinsic diffusion coefficients at the Kirkendall plane as constraints demonstrating the need for these diffusion parameters for generating a reliable mobility database. The direct estimation and optimization of diffusion coefficients without using thermodynamic details is an added advantage, especially in multicomponent alloy systems. © 2022 Acta Materialia Inc.

[error in script]
IITH Creators:
IITH CreatorsORCiD
Bhattacharya, SaswataUNSPECIFIED
Item Type: Article
Uncontrolled Keywords: Diffusion, Multicomponent alloy, PDE-constrained optimization
Subjects: Others > Metallurgy Metallurgical Engineering
Materials Engineering > Materials engineering
Divisions: Department of Material Science Engineering
Depositing User: . LibTrainee 2021
Date Deposited: 25 Jun 2022 06:01
Last Modified: 27 Jun 2022 05:15
URI: http://raiithold.iith.ac.in/id/eprint/9391
Publisher URL: https://doi.org/10.1016/j.scriptamat.2022.114639
OA policy: https://v2.sherpa.ac.uk/id/publication/4703
Related URLs:

Actions (login required)

View Item View Item
Statistics for RAIITH ePrint 9391 Statistics for this ePrint Item