Cross-attention-based multi-sensing signals fusion for penetration state monitoring during laser welding of aluminum alloy

Abstract

A precision multi-sensor monitoring strategy is required to meet the challenges posed by increasingly complex products and manufacturing processes during laser welding. In this work, an acoustic sensor and a photoelectric sensor were adopted to collect the signals during the laser welding of aluminum alloy. The dataset was divided into three categories according to the morphologies of the top and back sides. The cross-attention fusion neural network (CAFNet) was proposed to interactively capture photoelectric and acoustic information for effective quality classification without prior time–frequency analysis and feature learning. Its effectiveness and superiority were compared with the five types of deep learning (DL) based methods. It demonstrates that the proposed CAFNet method achieved a mean testing accuracy of 99.73% and a standard deviation of 0.37%, which outperforms other compared models. At the same time, the proposed CAFNet achieved the highest average testing accuracy of 94.34% when utilizing limited and imbalanced data, which suggested that the proposed method has stronger robustness than other methods. This approach is a new paradigm in the monitoring of laser welding and can be exploited to provide feedback in a closed-loop quality control system.