Melt Pool

Melt pool dynamics in additive manufacturing (AM) are crucial for ensuring part quality and process stability, driving research focused on real-time monitoring and prediction of its characteristics. Current efforts leverage machine learning, particularly deep learning models like convolutional neural networks (CNNs), vision transformers, and recurrent neural networks, to analyze high-speed imaging data and predict melt pool temperature, geometry, and potential defects from process parameters. These advancements enable faster, more accurate process control and optimization, leading to improved part quality and reduced defects in AM processes.