Davidlequnchen/LDED-FusionNet
LDED-FusionNet: Machine Learning-Based Audio-Visual Defect Detection for LDED AM Process
This tool helps manufacturing engineers and quality control specialists detect defects and predict quality in Laser-Directed Energy Deposition (LDED) additive manufacturing. By analyzing in-situ acoustic signals and visual melt pool images, it identifies anomalies and provides insights into the quality of the deposited material. It's designed for professionals overseeing additive manufacturing processes.
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Use this if you need to automatically identify defects and predict quality in real-time during Laser-Directed Energy Deposition using a combination of sound and video data.
Not ideal if you are working with other additive manufacturing processes besides LDED or if you only have a single sensor type (e.g., only visual data without corresponding audio).
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Jupyter Notebook
License
MIT
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Last pushed
Mar 27, 2025
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