Improving the predictive response using ensemble empirical mode decomposition based soft sensors with auto encoder deep neural network

• DOI: 10.1016/j.measurement.2022.111308
• Publication Date: 2022-05-19T17:28:21Z
• Authors: Sulaima Lebbe Abd...; Suhasini Sodagudi; Sara A Althubiti; Surendra Kumar Sh...; Mohammed Altaf Ahmed; Bharatiraja Chokk...
• Keywords: Pulsed laser welding; Weld penetration; Keyhole behavior; 0202 electrical engineering, electronic engineering, information engineering; Feature extraction; 02 engineering and technology; Convolution neural network; 620

The keyhole instability is a key concern in laser deep-penetration welding of high reflectivity materials, potentially impacting the penetration status and weld quality. Monitoring and control the keyhole behavior still remain a great challenge for obtaining a desired welded joint. For the pulsed laser welding of thin-sheet aluminum alloy, an active visual monitoring system was established to systematically probe the dynamic keyhole behavior from multi-view sensing. Combining with the image processing method and process analysis, the keyhole surface area and depth were extracted to quantify the keyhole formation dynamics under different welding conditions. Furthermore, a data-driven deep learning model with hyperparameter optimization was constructed to identify different penetration states and it has a high accuracy and good reliability. The experiment results show that our proposed measurement scheme based on multi-view monitoring and deep learning approach could guide the development of real-time control of the pulsed laser welding process.