Image Segmentation for Defect Analysis in Laser Powder Bed Fusion: Deep Data Mining of X-Ray Photography from Recent Literature
Source abstract
Abstract
The in situ X-ray imaging method has attracted significant attention in the metal additive manufacturing community for characterizing keyhole dynamics and defect generation during laser-material interaction processes, particularly for laser powder bed fusion. Due to a high temporal and spatial resolution in this method, a vast volume of data are generated and collected, leading to a challenge for data processing and analysis. In this study, we present an accurate, robust, and powerful image analytical approach that can identify the high-fidelity automated features and extract important information from X-ray images. In total, we train six semantic segmentation models and six object detection models using 628 X-ray images obtained from two recent literature. Our study demonstrates that the U net + MobileNet model is the overall best choice among 12 models to recognize and extract desired regions, in terms of accuracy, time consumption, and dataset sensitivity. Using this model, we have collected and summarized geometric features and dynamic behaviors of the keyholes and generated bubbles. The image segmentation approach may pave the path for unveiling new mechanisms that might not be easily identified using conventional analysis methods in additive manufacturing processes.
Source: expertdata · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Additive Manufacturing Materials and Processes · Advanced X-ray and CT Imaging · Laser Material Processing Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (41)
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