Modelling of geometry for directed energy deposition via machine learning - Hong, Weidong (Nanyang Technological University, 2023)
Predicting the geometry of bead in multi-track and multi-layer Directed Energy Deposition (DED) presents a challenge due to variations in process parameters, resulting in changes in geometry from one layer or track to another. To address this issue, a Long Short-Term Memory (LSTM) model is applied to model and predict the bead geometry in multi-track and multi-layer deposition, specifically height and waviness by predicting the 2nd order polynomial coefficients. The LSTM model is designed to capture long-term dependencies in the dataset containing process parameters and corresponding bead geometries. The process parameters used in this study include laser power, scanning speed, powder mass flow rate, hatch spacing value and z-incremental value. The applied model is trained, validated and tested using process parameters and the corresponding coefficients of unique sample name and layer number combination.