Paper Titles
TiO2- Polyurethane Cocopol Blend Nanocomposites as an Anticorrosion Coating for Mild Steel
p.43
Effect of Gadolinium Content on the Microstructures and Corrosion Properties of Mg-4Zn-3Gd Alloy
p.51
Corrosion Inhibitive Effects of Orange Juice for Brass in Acidic Medium 5M H2SO4
p.59
Investigation of the Adsorption Characteristics of Theobroma cacao Peels Biomass Inhibitors on Mild Steel Surfaces by EIS, SEM-EDX, XPS, and Chemical Studies in Acidic Media
p.69
Effects of Sn on Corrosion Resistance of Rare-Earth-Free Mg-2Zn Alloy
p.83
Mitigation of Erosion Wear Produced by Fly-Ash Slurry on 900 Elbow by Changing the Cross-Section Shape and Area Ratio
p.93
Integrated Phase Field and Machine Learning Study of Microstructure Evolution during Interface-Controlled Spinodal Decomposition
p.101
Exploring Sintering, Densification, and Mechanical Behaviour of Invar Alloys through Molecular Dynamics Simulations
p.107
Thermostamping Simulation of a Carbon Fiber-Reinforced PAEK Composite Stringer
p.113

Integrated Phase Field and Machine Learning Study of Microstructure Evolution during Interface-Controlled Spinodal Decomposition

Article Preview

Abstract:

This study leverages artificial intelligence (AI) to advance materials science, focusing on microstructural evolution in binary alloys during spinodal decomposition. Following the formulation of Zhu et al., we explore the microstructure evolution during interface-controlled spinodal decomposition. A comprehensive dataset captures the dynamic microstructural changes, highlighting the model's efficiency in analyzing complex data. The innovative use of an Autoencoder- ConvLSTM model enables precise, low-error microstructural transformation predictions, demonstrating AI’s potential in materials science research. This work provides a deeper understanding of material behaviors and offers new research directions.

You might also be interested in these eBooks
The 9th International Conference on Composite Materials and Material Engineering & The 14th International Conference on Advanced Materials Research View Preview
Advanced Materials Research and Corrosion Protection View Preview

Info:

Periodical:

Solid State Phenomena (Volume 357)

Pages:

101-106

DOI:

https://doi.org/10.4028/p-6W4IXl

Citation:

Cite this paper

Online since:

June 2024

Authors:

Owais Ahmad*, Rakesh Maurya, Rajdip Mukherjee, Somnath Bhowmick

Keywords:

Autoencoder, CONVLSTM, Interface Mobility, Machine Learning, Microstructure, Phase Field Model, Spinodal Decomposition

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

© 2024 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

References

[1] J. Zhu, L.-Q. Chen, J. Shen, and V. Tikare, "Coarsening kinetics from a variable-mobility cahn-hilliard equation: Application of a semi-implicit fourier spectral method," Physical Review E, vol. 60, no. 4, p.3564, 1999.

DOI: 10.1103/physreve.60.3564

Google Scholar

[2] N. Moelans, B. Blanpain, and P. Wollants, "An introduction to phase-field modeling of microstructure evolution," Calphad, vol. 32, no. 2, p.268–294, 2008.

DOI: 10.1016/j.calphad.2007.11.003

Google Scholar

[3] J. W. Cahn and J. E. Hilliard, "Free energy of a nonuniform system. i. interfacial free energy," The Journal of chemical physics, vol. 28, no. 2, p.258–267, 1958.

DOI: 10.1063/1.1744102

Google Scholar

[4] L. Q. Chen and J. Shen, "Applications of semi-implicit fourier-spectral method to phase field equations," Computer Physics Communications, vol. 108, no. 2-3, p.147–158, 1998.

DOI: 10.1016/s0010-4655(97)00115-x

Google Scholar

[5] M. M. Taye, "Theoretical understanding of convolutional neural network: Concepts, architectures, applications, future directions," Computation, vol. 11, no. 3, p.52, 2023.

DOI: 10.3390/computation11030052

Google Scholar

[6] X. Shi, Z. Chen, H. Wang, D.-Y. Yeung, W.-K. Wong, and W.-c. Woo, "Convolutional lstm network: A machine learning approach for precipitation nowcasting," Advances in neural information processing systems, vol. 28, 2015.

Google Scholar

[7] O. Ahmad, N. Kumar, R. Mukherjee, and S. Bhowmick, "Accelerating microstructure modeling via machine learning: A method combining autoencoder and convlstm," Phys. Rev. Mater., vol. 7, p.083802, Aug 2023.

DOI: 10.1103/physrevmaterials.7.083802

Google Scholar