Spatial Annotation of Time Series for Data Driven Quality Assurance in Additive Manufacturing
Reisch RT, Pantano M, Janisch L, Knoll A, Lee D (2023)
Publication Type: Conference contribution
Publication year: 2023
Journal
Publisher: Elsevier B.V.
Book Volume: 118
Pages Range: 753-758
Conference Proceedings Title: Procedia CIRP
Event location: Naples, ITA
DOI: 10.1016/j.procir.2023.06.129
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.
Involved external institutions
Germany (DE)How to cite
APA:
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 Roberto Teti, Doriana D'Addona (Eds.), Procedia CIRP (pp. 753-758). Naples, ITA: Elsevier B.V..
MLA:
Reisch, Raven T., et al. "Spatial Annotation of Time Series for Data Driven Quality Assurance in Additive Manufacturing." Proceedings of the 16th CIRP Conference on Intelligent Computation in Manufacturing Engineering, CIRP ICME 2022, Naples, ITA Ed. Roberto Teti, Doriana D'Addona, Elsevier B.V., 2023. 753-758.
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