Bead morphology prediction of coaxial laser cladding on inclined substrate using machine learning

Abstract

Laser cladding technology has been widely used in the repair and surface strengthening of huge parts. In order to avoid interference between the laser head and the surface to be deposited, the laser cladding process needs to be completed in a tilted position in order to remanufacture some complex parts. The morphology of the cladding layer directly affects the overall quality of the surface after laser cladding. To solve the problem that the quality of the coating cannot be guaranteed under an inclined attitude, SVR, PSO-BPNN, and XGBoost models were applied to build a prediction model between the bead morphology and process parameters. An L36 orthogonal array with four factors and three levels was used to obtain the original data set, and the four-fold cross-validation method was used to train and validate the three models. The predictive performance showed that the XGBoost model had better performance in predicting the morphology of the cladding layer on the inclined substrate compared to the other two algorithms. The R2 values of the width, height, and peak shifting point prediction model using XGBoost were 0.9578, 0.9419, and 0.8765 respectively. Finally, the prediction accuracy of the XGBoost model was evaluated by using other 9 test data sets. The maximum error in width was 3.73 %, and the errors in height and peak shifting point were less than 10 %. The prediction model also performed well in terms of MAE, RMSE, and R2 by using other nine test data sets, further demonstrating the generalization capability of XGBoost. This prediction model can be used to optimize the process path of la-ser cladding with a tilted position and effectively improve the processing efficiency and quality of complex surface laser cladding.