Experimental study and machine learning model to predict formability of magnesium alloy sheet

Background: Magnesium alloy is not only light in weight but also possesses moderate strength. AZ31-H24 sheet has many applications the automotive and aerospace industries. Experimental stretch forming tests are performed on this to measure material’s formability by constructing limit diagrams. Methods: Several of Nakazima were carried out rectangular samples at 24, 250, 350°C 0.01, 0.001 mm/s using a hemispherical punch. The work done predict magnesium alloys been recorded recent literature machine learning models. Hence, researchers article choose explore same build three models through Random Forest algorithm, Extreme Gradient Boosting, Multiple linear Regression. Results: The showed high accuracy 96% prediction. Conclusions: It concluded that need for physical experiments can be greatly minimized studies concepts.