{"id":3264,"date":"2022-07-13T09:30:00","date_gmt":"2022-07-13T07:30:00","guid":{"rendered":"https:\/\/cirpicme.org\/?page_id=3264"},"modified":"2022-07-26T13:02:40","modified_gmt":"2022-07-26T11:02:40","slug":"spatial-annotation-of-time-series-for-data-driven-quality-assurance-in-additive-manufacturing","status":"publish","type":"page","link":"https:\/\/cirpicme.org\/index.php\/additive-manufacturing-3\/spatial-annotation-of-time-series-for-data-driven-quality-assurance-in-additive-manufacturing\/","title":{"rendered":"Spatial Annotation of Time Series for Data Driven Quality Assurance in Additive Manufacturing"},"content":{"rendered":"\n

by Raven T. Reisch, Matteo Pantano, Lucas Janisch, Alois Knoll, Dongheui Lee (Germany)<\/em><\/p>\n\n\n\n

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Abstract<\/strong><\/p>\n\n\n\n

One of the biggest challenges for artificial intelligence in industry is the lack of labeled application data. Particularly for time series data, labeling requires a large amount of time for data preparation and expert knowledge both in data analysis and in the application domain. In this work, we propose a methodology for labeling time series solving the two barriers identified above in an additive manufacturing use case. Our approach correlates spatial and temporal features of process defects by means of a spatial sensor. By applying our method, we were able to achieve shorter labeling time while obtaining high-quality labels.<\/p>\n\n\n\n

Keywords<\/strong>: Labeling, Time Series, Direct Energy Deposition, Quality Assurance, Spatio-Temporal, Spatial Sensors<\/p>\n\n\n\n

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Video presentation<\/strong><\/p>\n\n\n\n