Online tensile strength prediction considering geometric and physical characteristics during FSW 2219-T8 aluminum alloy plate

Abstract

Tensile strength of joints of friction stir welded (FSW) thick 2219-T8 aluminum alloy plates, is affected by multiple geometric and physical characteristics, such as axial force, welding temperature, gap, and mismatch of the butt face, which makes it difficult to predict the tensile strength with high precision on line. At present, simple welding process prediction based on temperature or force is difficult to achieve accurate control of complex welding process. In this paper, a multi-information fused one-dimensional Convolutional Neural Network (1DCNN), combining geometric and physical characteristics of welding process, was proposed to predict the tensile strength of joints. Firstly, the experiments of FSW thick 2219-T8 aluminum alloy plates were conducted and the axial force, welding temperature, gap and mismatch of the butt face are measured. Then, the measured multi-source data was fused through down-sampling technology. Before training, the Particle Swarm Optimization (PSO) and Long Short-Term Memory (LSTM) were adopted to optimize the initial learning rate and layer structure of the model. The experiment results showed that the proposed model can accurately and quickly predict the tensile strength of thick 2219-T8 aluminum alloy joints with the mean absolute percentage error less than 2%. In addition, metallographic analysis revealed that the joint tensile strength is interlocked with weld defects and fine grain strengthening. The research lays a foundation for welding quality control of FSW.