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<p>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 (&lt; 0.3% for both the elastic matrix <b>C</b> and the compliance matrix <b>S</b>), making it a promising tool for the expedited design of microstructures with tailored properties.</p>

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An AI framework for time series microstructure prediction from processing parameters

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月5日

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  • Yuwei Mao,
  • Mahmudul Hasan,
  • Md Maruf Billah,
  • Youjia Li,
  • Sayak Chakrabarty,
  • Claire Songhyun Lee,
  • Kewei Wang,
  • Muhammed Nur Talha Kilic,
  • Vishu Gupta,
  • Wei-keng Liao,
  • Alok Choudhary,
  • Pinar Acar,
  • Ankit Agrawal

摘要

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.