Prediction of melt pool morphology of Ti alloy based on deep learning method

Abstract

This paper utilizes an artificial neural network (ANN) model to predict the melt pool morphology (including melt pool width, height and depth) of Titanium (Ti) alloy by adjusting welding process parameters (laser power, welding current, and welding speed). The correlation between welding process parameters and melt pool morphology are analyzed using the Pearson correlation coefficient, which the result indicates that the input features are directly related to the output features. Four deep learning (DL) models are evaluated, which ANN model has higher coefficient of determination (0.8725 and 0.9691), lower root mean square error (RMSE) (0.4322mm and 0.1405mm) and lower mean absolute error (MAE) (0.025mm and 0.112mm). When the number of hidden layers is five, the R2 and RMSE values of ANN model are 0.8024, 0.9838, 0.5356mm and 0.1019mm, respectively. To assess the accuracy of the model, experiments are conducted to validate the predicted values, and the errors between the experimental and predicted values are 0.01mm, 0.10mm, and 0.02mm, respectively.