An AI framework for time series microstructure prediction from processing parameters.

Journal: Scientific reports
Published Date: Jul 5, 2025

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.

Authors

  • Yuwei Mao
    ECE Department, Northwestern University, Evanston, Illinois 60208, United States.
  • Mahmudul Hasan
    Comcast Labs, Washington, DC, USA.
  • Md Maruf Billah
    Department of Mechanical Engineering, Virginia Tech, Blacksburg, USA.
  • Youjia Li
    Department of Neurology, First People's Hospital of Zhaoqing, Zhaoqing, 526000, China. lyj2102353@163.com.
  • Sayak Chakrabarty
    Department of Electrical and Computer Engineering, Northwestern University, Evanston, USA.
  • Claire Songhyun Lee
    Department of Electrical and Computer Engineering, Northwestern University, Evanston, USA.
  • Kewei Wang
    ECE Department, Northwestern University, Evanston, Illinois 60208, United States.
  • Muhammed Nur Talha Kilic
    Department of Electrical and Computer Engineering, Northwestern University, Evanston, USA.
  • Vishu Gupta
    Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, 60208, USA.
  • Wei-Keng Liao
    Northwestern University, Evanston, IL 60201 USA.
  • Alok Choudhary
    Northwestern University, Evanston, IL 60201 USA.
  • Pinar Acar
    Department of Mechanical Engineering, Virginia Tech, Blacksburg, Virginia, USA.
  • Ankit Agrawal
    Northwestern University, Evanston, IL 60201 USA.

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