搅拌摩擦点焊熔深预测及焊缝几何分析的机器学习算法
Machine Learning Algorithms for Prediction of Penetration Depth and Geometrical Analysis of Weld in Friction Stir Spot Welding Process
摘要：
如今，制造业利用机器学习和数据科学算法的力量来预测制造机械部件的机械和微观结构性能的优化。这些算法的应用在减少实验时间的同时也降低了实验成本。目前的研究工作是基于预测的渗透深度使用监督机器学习算法，如支持向量机（SVM），随机森林算法，和鲁棒回归算法。采用搅拌摩擦点焊技术对AA1230铝合金进行了焊接。该数据集由三个输入参数组成：转速（rpm）、停留时间（秒）和轴向载荷（KN），机器学习模型在此基础上进行训练和测试。它观察到，稳健回归机器学习算法的决定系数为0.96，优于其他算法。研究工作还强调了应用图像处理技术来发现焊缝成形的几何特征。
英文摘要：
Nowadays, manufacturing sectors harness the power of machine learning and data science algorithms to make predictions for the optimization of mechanical and microstructure properties of fabricated mechanical components. The application of these algorithms reduces the experimental cost beside leads to reduce the time of experiments. The present research work is based on the prediction of penetration depth using Supervised Machine Learning algorithms such as Support Vector Machines (SVM), Random Forest Algorithm, and Robust Regression algorithm. A Friction Stir Spot Welding (FSSW) was used to join two elements of AA1230 aluminum alloys. The dataset consists of three input parameters: Rotational Speed (rpm), Dwelling Time (seconds), and Axial Load (KN), on which the machine learning models were trained and tested. It observed that the Robust Regression machine learning algorithm outperformed the rest of the algorithms by resulting in the coefficient of determination of 0.96. The research work also highlights the application of image processing techniques to find the geometrical features of the weld formation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
