Machine Learning-Assisted Optimisation of the Laser Beam Powder Bed Fusion (PBF-LB) Process Parameters of H13 Tool Steel Fabricated on a Preheated to 350 ^∘C Building Platform.

ABSTRACT: This study presents the first application of Machine Learning (ML) models to optimise Powder Bed Fusion using Laser Beam (PBF-LB) process parameters for H13 steel fabricated on a 350 °C preheated building platform. A total of 189 cylindrical specimens were produced for training and testing machine learning (ML) models using variable process parameters: laser power (250-350 W), scanning speed (1050-1300 mm/s), and hatch spacing (65-90 μm). Eight ML models were investigated: 1. Support Vector Regression (SVR), 2. Kernel Ridge Regression (KRR), 3. Stochastic Gradient Descent Regressor, 4. Random Forest Regressor (RFR), 5. Extreme Gradient Boosting (XGBoost), 6. Extreme Gradient Boosting with limited depth (XGBoost LD), 7. Extra Trees Regressor (ETR) and 8. Light Gradient Boosting Machine (Lig

SUBMITTER: Kosarava K 

PROVIDER: S-EPMC12787040 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature