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Applying Natural Language Processing in Manufacturing

May, Marvin Carl ORCID iD icon 1; Neidhöfer, Jan 1; Körner, Tom 1; Schäfer, Louis 1; Lanza, Gisela 1
1 Institut für Produktionstechnik (WBK), Karlsruher Institut für Technologie (KIT)

Abstract:

Despite great progress in the digitization of the industrial sector through Industry 4.0 and widely available data, data analysis is typically constrained to numerical data, not the synthesis of knowledge. Although valuable employee knowledge in manufacturing is often described textually, it is rarely formalized and effective application hindered. To close this gap, we introduce methods of Natural Language Processing (NLP) to leverage available text data in manufacturing. For this purpose, we develop a NLP pipeline to handle textual information from machine providers. We extend this with production specific information to reduce failure downtime. The resulting, formalized knowledge can furthermore be used as a basis for optimizing manufacturing processes.


Verlagsausgabe §
DOI: 10.5445/IR/1000153659
Veröffentlicht am 09.12.2022
Originalveröffentlichung
DOI: 10.1016/j.procir.2022.10.071
Scopus
Zitationen: 8
Dimensions
Zitationen: 8
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Produktionstechnik (WBK)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2022
Sprache Englisch
Identifikator ISSN: 2212-8271
KITopen-ID: 1000153659
Erschienen in Procedia CIRP, 10th CIRP Global Web Conference – Material Aspects of Manufacturing Processes
Verlag Elsevier
Band 115
Seiten 184–189
Vorab online veröffentlicht am 07.11.2022
Schlagwörter Natural Language Processing, Predictive Maintenance, Knowledge Extraction, Machine Learning, Production Planning
Nachgewiesen in Scopus
Dimensions
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