Gaussian process regression to predict the morphology of friction-stir-welded aluminum/copper lap joints
Gaussian process regression to predict the morphology of friction-stir-welded aluminum/copper lap joints
复制标题
DOI：
10.1007/s00170-018-03229-1
复制
发表时间：
2019-01
期刊：
影响因子：
--
通讯作者：
M. Krutzlinger;E. Meltzer;M. Muehlegg;M. F. Zaeh
中科院分区：
文献类型：
--
作者：
M. Krutzlinger;E. Meltzer;M. Muehlegg;M. F. Zaeh
具有不同或甚至竞争性质的材料的接合在资源高效生产方面具有高度的工业利益。搅拌摩擦焊（FSW）已被用来创建不同材料组合的高质量接头。许多研究报告冶金结合和形状配合是相关的连接机制。虽然冶金结合是由相互扩散驱动的，并且几乎在每种情况下都会发生，但形状配合只有在界面变形时才能出现。变形界面的钩导致互锁;然而，它们也导致应力集中增加。因此，取决于它们的几何形状，钩接可以增强或降低接合强度。本研究展示了一种方法来预测搅拌摩擦焊接多材料接头的横截面界面面积的形态。采用图像处理技术将铝/铜搭接接头的横截面图像转换为二值B/W图像。采用高斯过程回归，基于13个数据集构建了界面区域形态的数据驱动模型。所得到的高斯过程模型的适用性进行了测试，通过比较该算法的形态预测与横截面焊接测试参数，不用于训练的七个数据集。这允许估计哪种连接机制对于整体连接强度是相关的或占主导地位的。计算结果与实际截面吻合较好。即使对于有限数量的训练数据，也成功地预测了界面区域的凹陷和钩。为了增强模型在后续应用中的可能使用空间（例如，断裂力学模拟），更多的输入参数可以被实现到模型中。
The joining of materials with different or even competing properties is of high industrial interest regarding resource-efficient production. Friction stir welding (FSW) has been employed to create high-quality joints of dissimilar material combinations. Many studies report both metallurgical bonding and form-fit to be the relevant joining mechanisms. While metallurgical bonding is driven by interdiffusion and occurs in almost every case, form-fit can only appear if the interface is deformed. The hooks of the deformed interface cause interlocking; however, they also result in an increased stress concentration. Hence, the hooking can either enhance or reduce the joint strength depending on their geometries. This study demonstrates an approach to predict the morphology of the cross-sectional interfacial area of friction-stir-welded multi-material joints. Image processing was used to convert cross sections of aluminum/copper lap joints into binary b/w images. Using Gaussian process regression, a data-driven model of the interfacial area’s morphology was constructed based on 13 data sets. The applicability of the resulting Gaussian process model was tested for seven data sets by comparing the algorithm’s morphological predictions with cross sections welded with test parameters that were not used for training. This allows to estimate, which joining mechanism is relevant or dominant for the overall joint strength. The predicted results agreed well with the actual cross sections. Recesses as well as hooks at the interfacial area were successfully predicted even for a limited number of training data. To enhance the space of possible uses of the model for subsequent applications (e.g., simulation of fracture mechanics), more input parameters can be implemented into the model.