Complex Networks; Smart Manufacturing; Industry 4.0
Résumé :
[en] With the advent of Industry 4.0, a growing number of sensors within modern production lines generate high volumes of data. This data can be used to optimize the manufacturing industry in terms of complex network topology metrics commonly used in the analysis of social and communication networks. In this work, several such metrics are presented along with their appropriate interpretation in the field of manufacturing. Furthermore, the assumptions under which such metrics are defined are assessed in order to determine their suitability. Finally, their potential application to identify performance limiting resources, allocate maintenance resources and guarantee quality assurance are discussed.
Disciplines :
Ingénierie mécanique
Auteur, co-auteur :
OMAR, Yamila ; University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit
MINOUFEKR, Meysam ; University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit
PLAPPER, Peter ; University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit
Co-auteurs externes :
no
Langue du document :
Anglais
Titre :
Lessons from social network analysis to Industry 4.0