ABSTRACT Additive friction stir deposition (AFSD) is an effective method for fabricating high‐performance deposits, with process parameters directly influencing the mechanical properties of the resulting samples. In this study, three machine learning models, that is, multilayer perception (MLP), radial basis function (RBF), and back propagation (BP), are developed to predict the ultimate tensile strength (UTS) and elongation (EL) of AFSD Al2219 samples. The input variables include set parameters (rotation speed, traverse speed, layer thickness, and the presence or absence of a preheating system as well as data obtained from an in situ process monitoring kit (temperature, feedstock force, deposition interface force, and deposition interface torque)). Results show that the BP‐trained neural network provides the best fit to the experimental data, achieving the highest coefficient of determination ( R 2 = 0.821 for UTS and 0.817 for EL), the lowest mean absolute error (MAE = 8.692 for UTS and 1.003 for EL), and the lowest root mean square error (RMSE = 13.773 for UTS and 1.266 for EL). These findings demonstrate the effectiveness and advantages of BP‐trained neural networks in predicting mechanical properties based on various input parameters. Recommendations are provided on how the prediction model can be applied in the field of additive manufacturing.
Tam et al. (Mon,) studied this question.