Mean field modeling of γʹ precipitate coarsening in additively manufactured Ni-base superalloy (18/09/25)
Speaker and Affliation:
Dr. Shailendra Verma
Graduate Institute of Ferrous and Eco Materials Technology, Pohang University of Science and Technology, South Korea
When?
18th September, 2025 (Thursday), 4.00 PM (India Standard Time)
Where
KPA Auditorium, Dept. of Materials Engineering, IISc, Bangalore
Abstract:
Additively manufactured (AM) Ni-base superalloys are emerging field of interest due to low production cost, less lead time, less wastage of materials, and intricate geometry fabrication. Therefore, in this research work, the γʹ precipitate coarsening kinetics in IN738LC Ni-base superalloy made by laser-powder bed fusion were predicted by one-dimensional mean field modeling and compared with the experimentally measured rate. A good agreement was observed between the predicted rate and the measured rate. It is found that during the initial stage of aging, the precipitate coarsening rate is faster for the AM superalloy compared to the superalloy having an equilibrium composition of phases. Therefore, the transition time from bimodal to unimodal distribution of precipitates is found to be much less compared to the conventionally cast superalloy. However, the steady state coarsening rate is the same for the superalloy made by both methods.
Speaker Bio:
Post-doctoral research at Graduate Institute of Ferrous and Eco Materials Technology, Pohang University of Science and Technology, South Korea (2023-2025) PhD from Department of Materials Engineering, Indian Institute of Science, Bangalore (2023) M. Tech (5 years integrated) in Industrial Chemistry from Indian Institute of Technology, Varanasi (2011)
Dr. Shailendra works in the design of materials for engineering, biomedical, and energy storage applications using Integrated Computational Materials Engineering (ICME). He studies Co and Ni-base superalloys, Al alloys, Ti alloys, steel, and energy storage materials. The computational methods consist of atomistic, continuum, and machine learning based modeling. His expertise is on atomistic simulations using first-principles DFT calculations, Monte Carlo, and Molecular Dynamics methods. Computational methods are used to model physical and mechanical properties. Experimental methods consist of vacuum arc melting, OM, SEM, TEM, XRD, APT, DSC, mechanical testing, etc.