Keyhole Depth Prediction in Laser Deep Penetration Welding of Titanium Alloy Based on Spectral Information
Year: 2025
Type: article
Abstract: Laser deep penetration welding has been widely applied in industrial fields. However, keyhole depth during the welding process significantly affects the service performance of final products. Based on the spectral signals generated in the laser welding process, this study employs a Long Short-Term Memory (LSTM) neural network to predict keyhole depth in titanium alloy welding. A coaxial plasma optical information acquisition system is established to collect spectral signals emitted from the welding plasma and analyze the relationship between keyhole depth and plasma spectral features. By analyzing the spectral signals and calculating the plasma temperature, the mapping model between the plasma temperature and keyhole depth is built. The LSTM network prediction results show that the average relative error between the predicted and actual values is 2.31%, which demonstrates that the method proposed in this study has high accuracy for predicting keyhole depth in laser deep penetration welding.
Source: Metals
DOI: 10.3390/met15050527
Authors: Yunqian Li, Yanfeng Gao, Hao Pan, Donglin Tao, Hua Zhang
Institution: Shanghai University of Engineering Science
Language: English
Subfield: Mechanical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W4410156320