NeroHin/defect-detection-and-segment-deep-learning
Detection and Segmentation in Powder Spreading Process of Magnetic Material Additive Manufacturing
This project helps quality control engineers and manufacturing specialists automatically identify and segment defects in digital images captured during the powder spreading process of magnetic material additive manufacturing. By analyzing images of the powder bed, it can detect issues like uneven powder distribution or uncovered areas, providing visual outputs that highlight the exact location and extent of these defects. This helps ensure quality and consistency in advanced manufacturing.
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Use this if you need to automate the detection and precise segmentation of defects in images from magnetic material additive manufacturing powder spreading.
Not ideal if you are working with a different material or manufacturing process, or if your images are not of powder spreading.
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Language
Jupyter Notebook
License
MIT
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Last pushed
Feb 20, 2023
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