Automatic melt pool tracking and segmentation in laser powder bed fusion using X-ray image sequence

Abstract Laser Powder Bed Fusion is among the most widely used techniques for metal additive manufacturing. In this process, a laser melts metal powder onto a substrate, forming a melt pool. The solid-liquid interface of the melt pool plays a critical role in the cooling behavior, which in turn affects the microstructure and mechanical properties of the printed part. High-speed X-ray imaging enables real-time observation of subsurface melt pool dynamics. However, accurately segmenting the melt pool from X-ray images remains challenging due to high noise levels and low contrast. Efficient data processing methods for this task are still underdeveloped. Researchers often rely on manual image masking or basic image processing techniques for object segmentation, which are either labor-intensive or lack sufficient accuracy and robustness. This study introduces a deep learning-based video object segmentation model that automatically tracks and segments the melt pool, thereby determining the solid-liquid interface in X-ray image sequences. The model is semi-supervised and highly efficient, requiring manual image masking only for the first frame to predict segmentations in subsequent frames. It incorporates spatiotemporal attention modules to capture correlations within the image sequence effectively. Specifically, a co-attention module extracts temporal features from the previous frame, while attention blocks highlight key regions in the current frame. Experimental results show that integrating attention mechanisms significantly improves segmentation accuracy compared to state-of-the-art methods.