A Machine Learning Approach for Predicting Melt-Pool Dynamics of Ti-6Al-4V Alloy in the Laser Powder-Bed Fusion Process

Abstract

Abstract This paper presents a supervised machine learning (ML) model to predict the melt-pool geometries of Ti-6Al-4V alloy in the laser powder-bed fusion (L-PBF) process. The ML model is developed based on the normalized values of the five key features (i.e., the laser and material parameters) — laser power, scanning speed, spot size, powder layer thickness, and powder porosity. The two target variables are the melt-pool width and depth, which define the melt-pool geometry and strongly correlate the geometry with the melt-pool dynamics. Information about the features and the corresponding target variables are compiled from an extensive literature survey. A trained data set is created with the melt-pool evolution data collected from experiments. The data set is divided into training and testing sets before any feature engineering, visualization, and analysis, to prevent any data leakage. The k-fold cross-validation technique is applied to minimize the error and find the best performance. Multiple regression methods are trained and tested to find the best model to predict the melt-pool geometry data. Extra trees regressor is found to be the model with the least amount of error using the mean absolute error function. The verification of the ML model is performed by comparing its results with the experimental and CFD modeling results for the melt-pool geometry at a given combination of the processing parameters in the L-PBF process. The melt-pool geometry outputs obtained for the ML model are consistent with the experimental and CFD modeling results.