An AI framework for time series microstructure prediction from processing parameters

In this study, we present an artificial intelligence (AI)-driven framework for predicting the microstructural texture of polycrystalline materials after a specific deformation process. The microstructural texture is defined in terms of the orientation distribution function (ODF) which indicates the volume density of crystal orientations. Our approach leverages an encoder-decoder model with Long Short-Term Memory (LSTM) layers to model the relationship between processing conditions and material properties. As a case study, we apply our framework to copper, generating a dataset of 3125 unique processing parameter combinations and their corresponding ODF vectors. The resulting predictions enable the calculation of homogenized properties. Our AI-driven framework outperforms traditional material processing simulations, yielding faster results with limited error rates (< 0.3% for both the elastic matrix C and the compliance matrix S), making it a promising tool for the expedited design of microstructures with tailored properties.

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Publication Details

Journal
Scientific Reports
Published
2025-07-05
DOI
https://doi.org/10.1038/s41598-025-06894-x
Citations
2
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
1.04

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article

An AI framework for time series microstructure prediction from processing parameters

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2 citations
Scientific Reports
Machine Learning in Materials Science
1.04
article

An AI framework for time series microstructure prediction from processing parameters

Youjia Li, Ankit Agrawal, Mahmudul Hasan, Pınar Acar, Vishu Gupta, Md. Maruf Billah, Wei‐keng Liao, Yuwei Mao, C. S. George Lee, Alok Choudhary, Kewei Wang, Muhammed Nur Talha Kilic, S. K. Chakrabarty
article en
2 citations

Abstract

In this study, we present an artificial intelligence (AI)-driven framework for predicting the microstructural texture of polycrystalline materials after a specific deformation process. The microstructural texture is defined in terms of the orientation distribution function (ODF) which indicates the volume density of crystal orientations. Our approach leverages an encoder-decoder model with Long Short-Term Memory (LSTM) layers to model the relationship between processing conditions and material properties. As a case study, we apply our framework to copper, generating a dataset of 3125 unique processing parameter combinations and their corresponding ODF vectors. The resulting predictions enable the calculation of homogenized properties. Our AI-driven framework outperforms traditional material processing simulations, yielding faster results with limited error rates (< 0.3% for both the elastic matrix C and the compliance matrix S), making it a promising tool for the expedited design of microstructures with tailored properties.

Scientific ReportsVol. 15(1)
Northwestern University (US), Virginia Tech (US)
National Science Foundation, U.S. Department of Energy, Northwestern University, National Institute of Standards and Technology, Division of Civil, Mechanical and Manufacturing Innovation
Openalex Percentile: Top 24%
Machine Learning in Materials Science
1.04
DOIOpen Access PDFOpenAlex
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