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Comprehensive process-molten pool relations modeling using CNN for wire-feed laser additive manufacturing
For the purpose of in situ quality control, the process parameters should be controlled in real-time based on sensed information from the process, in particular the molten pool.
Machine learning based in situ quality estimation by molten pool condition-quality relations modeling using experimental data
To enable in situ quality monitoring of bead geometry and characterization properties, we need to continuously monitor the sensor's data for molten pool dimensions and temperature for the Wire-feed laser additive manufacturing (WLAM) system.
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