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<p>Considering growing demand for accurate microstructural evolution models for metal additive manufacturing (MAM). In present work, a new approach is developed to combine data driven RGB (Red–Green–Blue) method and Markov random field (MRF), and physics-based phase field model (PFM) for microstructural evolution during laser directed energy depositions (LDED) based MAM at meso-microscale. RGB and MRF aims to capture different cliques in melt-pool possessing similar features and, PFM incorporates heterogeneous Gaussian mode of nucleation and physics driven grain-growth model based on temperature distribution to capture microstructural features. The output of data-driven models offers a flexible calibration of nucleation and grain growth parameters of PFM for improved prediction of grain growth during the solidification. The predicted microstructure by deploying above-mentioned model finds close match with the microstructural features of LDED built Ni bulk structure. The calculated error percentage of simulated grain feature with actual optical micrograph is 21 ± 4%, 24 ± 7%, 5 ± 3% and 26 ± 3% in SDAS, PDAS, coarse equiaxed and finer equiaxed region, respectively. This study offers an integration of data driven and physics driven model for an efficient and improved tool for studying the microstructural evolution at any location in additively manufactured components.</p>

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A novel coupled model for microstructural evolution prediction during laser additive manufacturing: data driven and physics driven modelling

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年1月20日

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  • S. Yadav,
  • A. K. Rai,
  • S. S. Kausal,
  • C. P. Paul

摘要

Considering growing demand for accurate microstructural evolution models for metal additive manufacturing (MAM). In present work, a new approach is developed to combine data driven RGB (Red–Green–Blue) method and Markov random field (MRF), and physics-based phase field model (PFM) for microstructural evolution during laser directed energy depositions (LDED) based MAM at meso-microscale. RGB and MRF aims to capture different cliques in melt-pool possessing similar features and, PFM incorporates heterogeneous Gaussian mode of nucleation and physics driven grain-growth model based on temperature distribution to capture microstructural features. The output of data-driven models offers a flexible calibration of nucleation and grain growth parameters of PFM for improved prediction of grain growth during the solidification. The predicted microstructure by deploying above-mentioned model finds close match with the microstructural features of LDED built Ni bulk structure. The calculated error percentage of simulated grain feature with actual optical micrograph is 21 ± 4%, 24 ± 7%, 5 ± 3% and 26 ± 3% in SDAS, PDAS, coarse equiaxed and finer equiaxed region, respectively. This study offers an integration of data driven and physics driven model for an efficient and improved tool for studying the microstructural evolution at any location in additively manufactured components.