Abstract
The large amount of strain combined with high temperature during Friction Stir Welding and Processing (FSWP) results in dynamic recrystallization and grain growth. The final properties of the processed material depend on the recrystallized grain structure. The ability to predict recrystallized microstructural features would take the FSWP modeling efforts one step closer to estimating the final weld mechanical properties. Here we present a computational framework for microstructural feature prediction based on the Discontinuous Dynamic Recrystallization (DDRX) principle considering plastic deformation, nucleation, and growth. The computed strains, strain rates and temperatures from an existing Heat Transfer and Material Flow (HTMF) model are utilized as input parameters for the DDRX model. The microstructural features such as average grain size, dislocation density, Taylor’s factor, number of new grains formation and grain size distribution are predicted using the DDRX model. The grain size prediction is validated against experimentally measured grain size, demonstrating a remarkable 97% accuracy and the reliability of the DDRX model.
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The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.
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Acknowledgements
The authors would like to thank DST FIST (SR/FST/ET-I/2017/18) grant for the development of an analytical FESEM equipped with an EBSD facility and the Central Instrumentation Facility at the Indian Institute of Technology Gandhinagar for providing the facility to perform EBSD Analysis. The authors would also like to extend gratitude to Dr. Vishvesh Badheka for providing the Friction Stir Welding facility at Pandit Deendayal Energy University to perform FSP trials.
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The work was financially supported by the Prime Minister’s Research Fellowship.
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P. S. worked on developing the model, validation, conducted the experiments for validation, analysis and writing the original draft. D. D. worked on developing the model and results analysis. A. A. worked on conceptualizing, manuscript revision and provided supervision.
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Sharma, P., Dhariwal, D. & Arora, A. Computational prediction of grain features during friction stir processes through a mechanistic discontinuous dynamic recrystallization model. Sci Rep (2026). https://doi.org/10.1038/s41598-026-38396-9
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DOI: https://doi.org/10.1038/s41598-026-38396-9
