Search

• Images of vapor plume in high-speed laser welding are captured through high-speed imaging. • Influences of laser power and welding speed on the behavior of vapor plume are analyzed. • Humping occurrence are predicted with the random forest and a sliding time window. The increase of welding speed can improve the productivity of laser welding. The behavior of vapor plume is an important indicator reflecting the state of the laser welding process. However, the behavior of vapor plume in high-speed laser welding (whose welding speed is higher than 10 m/min) has not been studied adequately. In this research, images of the vapor plume in high-speed laser welding of SUS304 austenitic stainless steel are captured through high-speed imaging for the monitoring of the welding process. Characteristics of the vapor plume are extracted with image processing, and the influences of laser power and welding speed on these characteristics are analyzed. The relationships between the behavior of vapor plume and melt pool are discussed, and three ejection regimes of the vapor plume are proposed. In order to predict the occurrence of humping, which is a typical defect in high-speed laser welding, samples are built by applying a time sliding window and then a classification model is built with the random forest. In addition, the importance of features in the classification is analyzed. The test results show that the proposed method can predict the humping defect in high-speed laser welding accurately. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

• Inconel 617 Ni-based superalloy sheets were joined using a laser-welding process under four different welding positions. • When using high-heat input parameters, the welding position has an obvious effect on the laser weld's quality. • Different welding positions generated different levels of hydrostatic melt pressure on the keyhole surface. Inconel 617 Ni-based superalloy sheets were joined using a laser-welding process. The welding experiments were conducted on four different welding positions (i.e., flat, horizontal, vertical-up, and vertical-down), using two sets of welding parameters with different heat inputs. Then, the geometry, internal defects, mechanical properties, and molten-pool dynamics of the laser joints were evaluated. The results showed that, when using high-heat input parameters, the welding position has an obvious effect on the laser weld quality. The top surface of the flat weld and the root surface of the vertical-down weld are both slightly concave. Moreover, the horizontal weld and vertical-down weld have serious keyhole-induced porosity defects, which lead to relatively worse mechanical properties. The analysis shows that the stability of keyhole differs for different welding positions; keyhole-induced porosity is easily generated in the horizontal and vertical-down positions. Moreover, solid metal blocks escape routes of the bubbles, resulting in more serious porosity for these welding positions. However, when using low-heat input parameters, the welding position has less influence on the laser weld quality because there is less molten metal, which makes the keyhole more stable in different welding positions. [ABSTRACT FROM AUTHOR]