@ARTICLE{Viswanadhapalli_B._Formability_2025,
 author={Viswanadhapalli, B. and Bupesh Raja, V.K. and Chaitanya, K. and Kannan, S.},
 volume={vol. 70},
 number={No 1},
 pages={183-192},
 journal={Archives of Metallurgy and Materials},
 howpublished={online},
 year={2025},
 publisher={Institute of Metallurgy and Materials Science of Polish Academy of Sciences},
 publisher={Committee of Materials Engineering and Metallurgy of Polish Academy of Sciences},
 abstract={Magnesium alloys has potential applications in aerospace and automotive industries as they are having good formability. Material properties like yield strength, ductility, have direct influence on material’s formability and product quality. At high temperature applications like aeroengine and steam engine, finding these properties are very crucial. For this purpose, uni-axial tension tests are performed at high temperatures on AZ31B magnesium alloy sheet to evaluate material formability properties. Finite element-based simulations have also been carried in LS Dyna program code. The output of the simulation is to find effective stresses and effective plastic strains. For this purpose, Tresca and Von Mises yielding conditions are utilized. These stresses are crucial in predicting and evaluating the forming limits of the material before necking. The results obtained from simulation code are consistent with experimental observations. An attempt has been made to predict formability by machine learning models. Random Forest shows the better model in predicting the formability. It has been concluded that the machine learning and Dyna code predictions has greatly minimises the physical experimentation.},
 title={Formability Studies on Magnesium Based AZ31B Alloy Sheet in LS Dyna Program Code},
 type={Article},
 URL={http://ochroma.man.poznan.pl/Content/134510/AMM-2025-1-20-Bupesh%20Raja.pdf},
 doi={10.24425/amm.2025.152532},
 keywords={Magnesium alloy sheet, uni-axial tension test, LS Dyna program code, machine learning model},
}