论文标题
用于预测渗透深度和焊接焊缝搅拌焊接过程焊接的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
论文作者
论文摘要
如今，制造领域利用机器学习和数据科学算法的功能，以对制造机械组件的机械和微观结构特性进行预测。这些算法的应用降低了实验成本，从而减少了实验时间。本研究工作基于使用有监督的机器学习算法（例如支持向量机（SVM），随机森林算法和鲁棒回归算法）对渗透深度的预测。使用摩擦搅拌点焊接（FSSW）连接了AA1230铝合金的两个元素。数据集由三个输入参数组成：旋转速度（RPM），住宅时间（秒）和轴向负载（kn），在该速度（kn）上训练和测试了机器学习模型。它观察到，可靠的回归机学习算法通过确定的系数为0.96来优于其余算法。该研究工作还强调了图像处理技术在寻找焊接形成的几何特征方面的应用。
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.
