npj：机器学习—焊接的空隙形成条件

在此过程中，凭压力将具有肩部和焊销的旋转刚性工具插入接头中。工具和工件之间的摩擦产生的热量使合金软化但不熔化。软化的材料围绕工具销流动并在销钉后面形成接头。由于摩擦搅拌焊接不涉及熔化，因此避免了常见的熔焊问题，例如凝固裂纹和挥发性合金元素的损失。尽管有许多优点，但它的成功取决于许多复杂物理过程的耦合，这些过程会影响温度的三维分布、材料的塑化速度、应变速率以及其他机械和冶金变量。温度和速度场，应变速率和其他参数的变化可能导致在靠近焊销尖端处的部件形成空隙。焊接部件中的空隙影响接头的机械性能和服役性能。 

该研究使用两种机器学习算法，即神经网络和决策树算法，研究了铝合金摩擦搅拌焊接中的空隙形成。来自美国宾夕法尼亚州立大学的Tarasankar DebRoy 领导的研究小组，分析了同行评审文献中108个独立的实验数据。他们研究了原始焊接参数和潜在的致因变量，如温度、刀具销上的最大剪切应力、扭矩和应变速率等。他们发现：

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.