Arif Sajjad, Samad Abdul, Muaz Muhammed, Khan Anwar Ulla, Khan Mohammad Ehtisham, Ali Wahid, Ahmad Farooque
Department of Mechanical Engineering, Aligarh Muslim University, Aligarh 202002, India.
Department of Electrical Engineering Technology, College of Applied Industrial Technology, Jazan University, Jazan 45142, Saudi Arabia.
Materials (Basel). 2024 Dec 29;18(1):94. doi: 10.3390/ma18010094.
This paper estimates friction stir welded joints' ultimate tensile strength (UTS) and hardness using six supervised machine learning models (viz., linear regression, support vector regression, decision tree regression, random forest regression, K-nearest neighbour, and artificial neural network). Tool traverse speed, tool rotational speed, pin diameter, shoulder diameter, tool offset, and tool tilt are the six input parameters in the 200 datasets for training and testing the models. Deep learning artificial neural networks (ANN) exhibited the highest accuracy. Therefore, the ANN approach was used successfully to estimate the UTS and the hardness of friction stir welded joints. Additionally, the relationship of pin diameter, tool offset, and tool rotation speed over UTS and hardness were extracted over the collected data points. Furthermore, experimental results, such as UTS and hardness of steel-magnesium-based welded joints and model estimated results, were compared to cross-check model generalization capability. It was noted that ANN estimates and experimental results at desired processing conditions are consistent with sufficiently high accuracy.
本文使用六种监督机器学习模型（即线性回归、支持向量回归、决策树回归、随机森林回归、K近邻和人工神经网络）来估计搅拌摩擦焊接接头的极限抗拉强度（UTS）和硬度。刀具行进速度、刀具转速、销钉直径、肩部直径、刀具偏移量和刀具倾斜度是200个数据集中用于训练和测试模型的六个输入参数。深度学习人工神经网络（ANN）表现出最高的准确性。因此，ANN方法成功地用于估计搅拌摩擦焊接接头的UTS和硬度。此外，通过收集的数据点提取了销钉直径、刀具偏移量和刀具转速与UTS和硬度之间的关系。此外，还比较了钢镁基焊接接头的UTS和硬度等实验结果与模型估计结果，以交叉检验模型的泛化能力。值得注意的是，在所需加工条件下，ANN估计值与实验结果具有足够高的一致性。
