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Materials Processing and Fundamental Understanding Based on Machine Learning and Data Informatics: Poster Session
Program Organizers: Kathy Lu, University of Alabama Birmingham; Jian Luo, University of California, San Diego; Xian-Ming Bai, Virginia Polytechnic Institute and State University; Yi Je Cho, Sunchon National University

Monday 5:00 PM
October 10, 2022
Room: Ballroom BC
Location: David L. Lawrence Convention Center

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B-5: Using Computer Vision and Machine Learning to Characterize Melt Pool Geometry in Additive Manufacturing: Han Chien¹; Bo Lei¹; Bryan Webler¹; Elizabeth Holm¹; ¹Carnegie Mellon University
    The dimension of the melt pool determines the initial geometry of the material deposition in the process of laser powder bed fusion (LPBF). It is crucial for quality control to identify the microstructural features by using computer vision techniques. A model is built for deep learning on image segmentation which can recognize the boundaries between the heat affected zone and the unaffected base metal. The model is able to identify the shape and size of the heat affected zone and thus the dimension of the melt pool can be calculated. This study gives insight into the image segmentation of the melt pool geometry and contributes to the dimension measurement of it.

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