Mechanical Engineering, Gokaraju Rangaraju Institute of Engineering and Technology, Hyderabad, TELANGANA, 500090, India.
School of Mechanical Engineering, Sathyabama Institute of Science and Technology, Chennai, Tamilnadu, 600119, India.
F1000Res. 2022 Sep 29;11:1118. doi: 10.12688/f1000research.124085.1. eCollection 2022.
Magnesium alloy is not only light in weight but also possesses moderate strength. Magnesium AZ31-H24 alloy sheet has many applications in the automotive and aerospace industries. Experimental stretch forming tests are performed on this sheet to measure the material's formability by constructing forming limit diagrams. Several tests of Nakazima were carried out on rectangular samples at 24, 250, 350°C and 0.01, 0.001 mm/s using a hemispherical punch. The work done to predict the formability of magnesium alloys has not been recorded in recent literature on machine learning models. Hence, the researchers of this article choose to explore the same and build three models to predict the formability of magnesium alloy through Random Forest algorithm, Extreme Gradient Boosting, and Multiple linear Regression. The Random Forest showed high accuracy of 96% in prediction. It is concluded that the need for physical experiments can be greatly minimized in formability studies by using machine learning concepts.
镁合金不仅重量轻，而且强度适中。AZ31-H24 镁合金板材在汽车和航空航天工业中有许多应用。通过构建成形极限图，对该板材进行了拉伸成形实验，以测量材料的成形性。在 24、250、350°C 和 0.01、0.001 mm/s 下，使用半球形冲头对矩形试样进行了多次 Nakazima 试验。在最近关于机器学习模型的文献中，没有记录用于预测镁合金成形性的功。因此，本文的研究人员选择探索相同的方法，并通过随机森林算法、极端梯度提升和多元线性回归构建三个模型来预测镁合金的成形性。随机森林在预测中的准确率高达 96%。结论是，通过使用机器学习概念，可以大大减少成形性研究中的物理实验需求。
