Penetration State Recognition during Laser Welding Process Control Based on Two-Stage Temporal Convolutional Networks.

ABSTRACT: Vision-based laser penetration control has become an important research area in the field of welding quality control. Due to the complexity and large number of parameters in the monitoring model, control of the welding process based on deep learning and the reliance on long-term information for penetration identification are challenges. In this study, a penetration recognition method based on a two-stage temporal convolutional network is proposed to realize the online process control of laser welding. In this paper, a coaxial vision welding monitoring system is built. A lightweight segmentation model, based on channel pruning, is proposed to extract the key features of the molten pool and the keyhole from the clear molten pool keyhole image. Using these molten pool and keyhole features, a

SUBMITTER: Liu Z 

PROVIDER: S-EPMC11433600 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature