Multi-Output Monitoring of High-Speed Laser Welding State Based on Deep Learning

In order to ensure the production quality of high-speed laser welding, it is necessary to simultaneously monitor multiple state properties. Monitoring methods combining vision sensing and deep learning models are popular but most models used can only make predictions on single welding state property. In this contribution, we propose a multi-output model based on a lightweight convolutional neural network (CNN) architecture and introduce the particle swarm optimization (PSO) technique to optimize the loss function of the model, to simultaneously monitor multiple state properties of high-speed laser welding of AISI 304 austenitic stainless steel. High-speed imaging is performed to capture images of the melt pool and the dataset is built. Test results of different models show that the proposed model can achieve monitoring of multiple welding state properties accurately and efficiently. In addition, we make an interpretation and discussion on the prediction of the model through a visualization method, which can help to deepen our understanding of the relationship between the melt pool appearance and welding state. The proposed method can not only be applied to the monitoring of high-speed laser welding but also has the potential to be used in other procedures of welding state monitoring.

Automated summary

This research proposes a multi-output model based on a lightweight CNN architecture and PSO technique for monitoring multiple state properties of high-speed laser welding, capturing melt pool images through high-speed imaging and building a dataset. The results show that the proposed model can accurately and efficiently monitor multiple welding state properties, and interpretation and discussion through visualization methods deepen the understanding of the relationship between melt pool appearance and welding state.