Data-Driven Approaches for Bead Geometry Prediction via Melt Pool Monitoring

Abstract

In additive manufacturing, choosing process parameters to prevent over and under deposition is a time and resource intensive trial-and-error process. Due to the uniqueness of each part geometry, further development of real-time process monitoring and control is needed for reliable part dimensional accuracy. This research shows that support vector regression (SVR) and convolutional neural network (CNN) models offer a promising solution for real-time process control due to the models' abilities to recognize complex, nonlinear patterns with high accuracy. A novel experiment was designed to compare the performance of SVR and CNN models to indirectly detect bead height from a coaxial image of a melt pool from a single layer, single bead build. The study showed that both SVR and CNN models trained on melt pool data collected from a coaxial optical camera can accurately predict the bead height with a mean absolute percentage error of 3.67% and 3.68%, respectively.