2020
DOI: 10.1007/s00779-020-01394-3
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Auditory augmented process monitoring for cyber physical production systems

Abstract: We describe two proof-of-concept approaches on the sonification of estimated operation states and conditions focusing on two scenarios: a laboratory setup of a manipulated 3D printer and an industrial setup focusing on the operations of a punching machine. The results of these studies form the basis for the development of an “intelligent” noise protection headphone as part of Cyber Physical Production Systems which provides auditorily augmented information to machine operators and enables radio communication b… Show more

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Cited by 10 publications
(5 citation statements)
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“…The findings of AARiP's proofs-of-concept serve as the foundation for creating intelligent noisecancelling headphones as a component of Cyber-Physical Production Systems (CPPSs), which give machine operators auditorily enhanced information and enable radio communication between them. While the results have not been validated in a user study yet, the studies were presented and approved by the respective scientific community (Iber et al 2019;Iber et al 2021).…”
Section: Aarip: Results and Investigation Of Narrative Elementsmentioning
confidence: 99%
“…The findings of AARiP's proofs-of-concept serve as the foundation for creating intelligent noisecancelling headphones as a component of Cyber-Physical Production Systems (CPPSs), which give machine operators auditorily enhanced information and enable radio communication between them. While the results have not been validated in a user study yet, the studies were presented and approved by the respective scientific community (Iber et al 2019;Iber et al 2021).…”
Section: Aarip: Results and Investigation Of Narrative Elementsmentioning
confidence: 99%
“…[2] also points towards benefits of adding sonification to visual representations, specifically targeted in security related process monitoring. In [13] additional evidence on performance improvement in process monitoring (of physical production systems) via sonification is provided.…”
Section: Related Workmentioning
confidence: 99%
“…n estimators : [50, 100, 150, 200, 250, 300], number of trees in the forests, -max depth : [5,10,20,30,50], maximum number of levels in each decision tree, -min samples split : [2,3,4,5,6,7], minimum number of data points placed in a node before the node is split, -min samples leaf : [1, 2, 3, 5], minimum number of data points allowed in a leaf node, -k : range(10, 30), number of features selected using KBest.…”
Section: B Hyperparameter Tuningmentioning
confidence: 99%