Online prediction of joint mechanical properties of FSW thick AA2219-T8 based on multi-source information fusion using 1DCNN

Abstract

High strength AA2219-T8, Al-Cu alloy of 18 mm plate is welding by friction stir welding (FSW) technology and used for heavy-lift launch vehicles tank. During FSW thick AA2219-T8 plate, joint tensile strength and microhardness are affected by multiple interconnected variables including welding force, temperature, and geometric quantity of the butt face. The joint mechanical properties are difficult to predict and ensure. This paper proposed a hybrid predicting approach based on multi-source information fusion, called Down-Sampling and One-Dimensional Convolutional Neural Network (DS-1DCNN). Initially, FSW experiments were conducted to collect the axial force, welding surface temperature at advancing and retreating side, gap, and mismatch signals, which were fused through DS technology. To extract temporal information and features from signals, a 1DCNN prediction model was established, with the fused multi-source signals as input and the tensile strength and Vickers hardness as output. Subsequently, the optimal model architecture and hyper-parameters of 1DCNN were determined through experiments and grid search combined with K-fold cross-validation, and an online prediction system was developed. Finally, effective validation experiments were conducted and the experimental results agreed with the DS-1DCNN prediction results with an acceptable error of approximately 5%. Parameter sensitivity analysis revealed that the number of convolutional layers and learning rate were identified as critical parameters affecting the model performance. Additionally, time analysis showed that the calculation time of the prediction system was less than 200 ms which can meet the time requirements of real-time thick-plate FSW application. The research lays a foundation on the real-time quality monitoring in the process of FSW thick-plate butt joint.