<div class="csl-bib-body">
<div class="csl-entry">Reisch, R. T., Pantano, M., Janisch, L., Knoll, A., & Lee, D. (2023). Spatial Annotation of Time Series for Data Driven Quality Assurance in Additive Manufacturing. In R. Teti & D. D’Addona (Eds.), <i>Procedia CIRP : 16th CIRP Conference on Intelligent Computation in Manufacturing Engineering</i> (pp. 753–758). Elsevier BV. https://doi.org/10.1016/j.procir.2023.06.129</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/193087
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dc.description.abstract
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.
en
dc.language.iso
en
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dc.subject
Direct Energy Deposition
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dc.subject
Labeling
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dc.subject
Quality Assurance
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dc.subject
Spatial Sensors
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dc.subject
Spatio-Temporal
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dc.subject
Time Series
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dc.title
Spatial Annotation of Time Series for Data Driven Quality Assurance in Additive Manufacturing
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dc.type
Inproceedings
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dc.type
Konferenzbeitrag
de
dc.relation.publication
Procedia CIRP : 16th CIRP Conference on Intelligent Computation in Manufacturing Engineering
