XGBoost-based on-line prediction of seam tensile strength for Al-Li alloy in laser welding: Experiment study and modelling

This paper studies the regression prediction of laser welding seam strength of aluminum-lithium alloy used in the rocket storage tank by means of the optical spectrum and extreme gradient boosting decision tree (XGBoost). First, the relationship between the spectrum intensity and the seam strength coefficient is thoroughly investigated through parameters changing experiments using the developed monitoring system of the optical spectrum. Then, the importance of the metal line spectrum, including Al I, Li I, and Mg I, is quantitatively evaluated, and good complementarity between the Random Fores(RF)t and Principal Component Analysis(PCA) is demonstrated. Finally, a novel regression model, e.g., RFPCA-XGBoost is proposed and is compared with other different feature selection methods, tree-based ensemble learning models and grid search parameters optimization, and the comparison results show that among all the methods, the proposed model has the best performance regarding the R2 value, achieving the R2 value of 0.9383.

Automated summary

This paper investigates the regression prediction of laser welding seam strength of aluminum-lithium alloy in rocket storage tanks using optical spectrum and XGBoost, proposing a novel regression model RFPCA-XGBoost with the best performance regarding R2 value of 0.9383 among all methods tested.