Keyhole Depth Prediction in Laser Deep Penetration Welding of Titanium Alloy Based on Spectral Information

This is the authors' abstract. We don't add key points for this paper.

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.