Friction stir welded joints often contain voids that are detrimental to their mechanical properties. Here we investigate the conditions for void formation using a decision tree and a Bayesian neural network. Three types of input data sets including unprocessed welding parameters and computed variables using an analytical and a numerical model of friction stir welding were examined. One hundred and eight sets of independent experimental data on void formation for the friction stir welding of three aluminum alloys, AA2024, AA2219, and AA6061, were analyzed. The neural network-based analysis with welding parameters, specimen and tool geometries, and material properties as input predicted void formation with 83.3% accuracy. When the potential causative variables, i.e., temperature, strain rate, torque, and maximum shear stress on the tool pin were computed from an approximate analytical model of friction stir welding, 90 and 93.3% accuracies of prediction were obtained using the decision tree and the neural network, respectively. When the same causative variables were computed from a rigorous numerical model, both the neural network and the decision tree predicted void formation with 96.6% accuracy. Among these four causative variables, the temperature and maximum shear stress showed the maximum influence on void formation.

搅拌摩擦焊接接头通常包含有害于其机械性能的空隙。在这里，我们使用决策树和贝叶斯神经网络研究空隙形成的条件。检查了三种类型的输入数据集，包括未处理的焊接参数和使用摩擦搅拌焊的解析模型和数值模型计算出的变量。分析了三种铝合金AA2024，AA2219和AA6061的摩擦搅拌焊接的一百零八套独立的实验数据。基于神经网络的分析，以焊接参数，样品和工具的几何形状以及材料属性为输入，预测空洞形成的准确性为83.3％。当潜在的原因变量，例如温度，应变率，扭矩，根据搅拌摩擦焊的近似分析模型计算出工具销上的最大剪切应力和最大剪切应力，使用决策树和神经网络分别获得90％和93.3％的预测准确度。当从严格的数值模型中计算出相同的因果变量时，神经网络和决策树均可以96.6％的精度预测空隙的形成。在这四个原因变量中，温度和最大剪切应力显示出对空隙形成的最大影响。神经网络和决策树均以96.6％的准确度预测了空隙的形成。在这四个原因变量中，温度和最大剪切应力显示出对空隙形成的最大影响。神经网络和决策树均以96.6％的准确度预测了空隙的形成。在这四个原因变量中，温度和最大剪切应力显示出对空隙形成的最大影响。