Predictive Manufacturing: Classification of categorical data

Publikation: Forskning - peer reviewTidsskriftartikel

Abstrakt

Today, advances in computing along with smart sensor technologies are redesigning the whole manufacturing paradigm. Advanced sensors have boosted the transition in manufacturing systems from semi-automated to fully automated manufacturing processes. The distinguishing feature of these automated processes is high volume of information about the process dynamics. In this paper we present a methodology to deal with the categorical data streams from manufacturing processes, with an objective of predicting failures on the last stage of the process. A thorough examination of the behaviour and classification capabilities of our methodology (on different experimental settings) is done through a specially designed simulation experiment. Secondly, in order to demonstrate the applicability in a real life problem a data set from electronics component manufacturing is being analysed through our proposed methodology.
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Detaljer

Today, advances in computing along with smart sensor technologies are redesigning the whole manufacturing paradigm. Advanced sensors have boosted the transition in manufacturing systems from semi-automated to fully automated manufacturing processes. The distinguishing feature of these automated processes is high volume of information about the process dynamics. In this paper we present a methodology to deal with the categorical data streams from manufacturing processes, with an objective of predicting failures on the last stage of the process. A thorough examination of the behaviour and classification capabilities of our methodology (on different experimental settings) is done through a specially designed simulation experiment. Secondly, in order to demonstrate the applicability in a real life problem a data set from electronics component manufacturing is being analysed through our proposed methodology.
OriginalsprogEngelsk
TidsskriftJournal of Quality Technology
ISSN0022-4065
StatusAfsendt - 2017
PublikationsartForskning
Peer reviewJa
ID: 250241641