Researchers have developed a transfer learning-enhanced physics-informed neural network (TLE-PINN) for predicting melt pool morphology in selective laser melting (SLM). This novel approach combines ...

The co-axially mounted CCD camera acquired the melt pool morphology corresponding to six build parameters covering the process map from Lack of Fusion (LoF) to conduction regime. The images acquired ...

for predicting melt pool morphology in selective laser melting (SLM). This novel approach combines physics-informed constraints with deep learning techniques, achieving superior accuracy, faster ...

However, accurately predicting melt pool morphology, a critical factor influencing material properties and process quality, remains a significant challenge. Traditional numerical simulations are ...