Charles Snyers

Prediction of Melt Pool Temperature for DED Using Supervised Learning Methods on Optical Measurement Data

Charles Snyers, Julien Ertveldt, Jorge Sanchez-Medina, Zoé Jardon and Jan Helsen

Published in Progress in Additive Manufacturing 2021, 2022

This paper details how an experimental dataset is produced by temperature estimation during the Directed Energy Deposition of 316L stainless steel. The temperature data is used as training data for supervised-learning methods, and a study is conducted to compare the performance of the considered methods using the measured optical data.

Prediction of Build Geometry for DED using Supervised Learning Methods on Simulated Process Monitoring Data

Charles Snyers, Julien Ertveldt, Jorge Sanchez-Medina, Zoé Jardon and Jan Helsen

Published in Journal of Laser Applications, 2021

This paper explains a method to produce artificial in-situ monitoring data of a metal AM process. A dataset produced with this method, is used to link process parameters and melt pool geometrical properties thanks to Machine Learning shallow algorithms.