Abstract
Current study results on Machine Learning have been published. According to news reporting originating from Jamshedpur, India, by NewsRx correspondents, research stated, “Manipulation of creep properties and microstructural transformations at different temperatures and applied stresses depicts huge importance for the design and development of various grades of metals and alloys. Therefore, we have considered nano-size face-centered cubic (FCC) single crystal of Fe-Cr-Ni alloy to investigate creep response under a wide range of temperatures and pressure through molecular dynamics (MD) simulation and regression-based machine learning methodologies.” Financial support for this research came from CSIR-NML. Our news editors obtained a quote from the research from the Council of Scientific and Industrial Research (CSIR), “From MD simulation, we have found the evolution of multiple rectangular blocks of body-centered cubic (BCC) crystal and layered FCC and HCP crystal during creep deformation under externally applied tensile load. Rectangular blocks and layered crystal structures corroborated with the secondary and tertiary stages of creep curves of Fe-Cr-Ni alloy, respectively. Machine learning methodology provides information to predict the creep properties and correlates data obtained from MD simulations.”
NETL
NSTL
Indian Acad Sciences