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What is Metal AM Quality Inspection with CNN about?

This study presents a convolutional neural network (CNN) approach for inspecting metal additive manufacturing (AM) parts, addressing the inefficiencies of traditional manual inspection methods. The CNN model achieved an accuracy of 92.1% in recognizing defects such as cracks, gas porosity, and lack of fusion, significantly improving the quality inspection process in the AM industry. The research highlights the importance of deep learning techniques in automating defect recognition and enhancing the reliabil

Author

mansi sinha

Language

EN