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- W4386597941 abstract "Despite the high potential of selective laser melting (SLM) in metal additive manufacturing (AM), there are still challenges in real-time process monitoring and quality control of SLM. In-situ layer-wise imaging is widely used to monitor the condition of the powder bed. The geometric contour of the part during SLM production is also a crucial monitoring metric. Current studies have used traditional image segmentation algorithms to extract part contours. This study proposes a deep learning approach using layer-wise monitoring images. The semantic segmentation model with attention mechanism is proposed to effectively and accurately recognize the part contour in the powder bed image. Its performance surpasses other classical models. A visual explanation of the model and a part contour prediction method for high-resolution powder bed images are also explored. These results show that the proposed method effectively recognizes layer-wise part contours, which is beneficial for the future implementation of SLM in-situ real-time process monitoring." @default.
- W4386597941 created "2023-09-12" @default.
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- W4386597941 date "2023-08-18" @default.
- W4386597941 modified "2023-09-27" @default.
- W4386597941 title "Deep Learning for Layer-wise Part Contour Recognition in Additive Manufacturing Process" @default.
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- W4386597941 doi "https://doi.org/10.1109/iciea58696.2023.10241485" @default.
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