UDAVA: An Unsupervised Learning Pipeline for Sensor Data Validation in Manufacturing

Manufacturing has enabled the mechanized mass production of the same (or similar) products by replacing craftsmen with assembly lines of machines. The quality of each product in an assembly line greatly hinges on continual observation and error compensation during machining using sensors that measur...

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Veröffentlicht in:	Proceedings of the 1st International Conference on AI Engineering: Software Engineering for AI S. 159 - 169
Hauptverfasser:	Husom, Erik Johannes, Tverdal, Simeon, Goknil, Arda, Sen, Sagar
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	ACM 01.05.2022
Schlagworte:	Behavioral sciences Cutting tools Data models Manufacturing processes Pipelines Production Sensors
Online-Zugang:	Volltext
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