论文标题
基于机器学习的算法和Taguchi算法的性能评估，以确定摩擦搅拌的硬度值在块区域的摩擦值AA 6262关节
Performance Evaluation of Machine Learning-based Algorithm and Taguchi Algorithm for the Determination of the Hardness Value of the Friction Stir Welded AA 6262 Joints at a Nugget Zone
论文作者
论文摘要
如今，行业4.0在制造行业中起着极大的作用，可以提高现代制造系统的数据和准确性。由于人工智能，尤其是机器学习，大数据分析已彻底修改，制造商很容易利用有组织的和无组织的数据。这项研究利用混合优化算法在掘金区域找到摩擦搅拌焊接和最佳硬度值。使用类似的AA 6262材料，并在对接接头配置中焊接。刀具旋转速度（RPM），刀具横幅速度（mm/min）和平面深度（mm）用作可控参数，并使用Taguchi L9，Random Forest和XG Boost机器学习工具进行了优化。还以95％的置信区间进行方差分析，以识别重要参数。结果表明，从Taguchi L9正交阵列确定的系数分别为0.91，而随机森林和XG增强算法分别赋予0.62和0.65。
Nowadays, industry 4.0 plays a tremendous role in the manufacturing industries for increasing the amount of data and accuracy in modern manufacturing systems. Thanks to artificial intelligence, particularly machine learning, big data analytics have dramatically amended, and manufacturers easily exploit organized and unorganized data. This study utilized hybrid optimization algorithms to find friction stir welding and optimal hardness value at the nugget zone. A similar AA 6262 material was used and welded in a butt joint configuration. Tool rotational speed (RPM), tool traverse speed (mm/min), and the plane depth (mm) are used as controllable parameters and optimized using Taguchi L9, Random Forest, and XG Boost machine learning tools. Analysis of variance was also conducted at a 95% confidence interval for identifying the significant parameters. The result indicated that the coefficient of determination from Taguchi L9 orthogonal array is 0.91 obtained while Random Forest and XG Boost algorithm imparted 0.62 and 0.65, respectively.
