Predictive Manufacturing: Classification of categorical data

Abdul Rauf Khan, Henrik Schiøler, Murat Kulahci, Torben Knudsen

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

Resumé

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 - 2019

Fingerprint

Smart sensors
Electronic equipment
Categorical data
Manufacturing
Methodology
Sensors
Sensor
Manufacturing process
Experiments
Data streams
Dynamic process
Paradigm
Manufacturing systems
Simulation experiment

Citer dette

Khan, A. R., Schiøler, H., Kulahci, M., & Knudsen, T. (2019). Predictive Manufacturing: Classification of categorical data. Manuskript afsendt til publicering.
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Predictive Manufacturing: Classification of categorical data. / Khan, Abdul Rauf; Schiøler, Henrik; Kulahci, Murat; Knudsen, Torben.

I: Journal of Quality Technology, 2019.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

TY - JOUR

T1 - Predictive Manufacturing: Classification of categorical data

AU - Khan, Abdul Rauf

AU - Schiøler, Henrik

AU - Kulahci, Murat

AU - Knudsen, Torben

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

KW - Big data analytics for industrial process, Data Mining, Genetic Algorithm, Machine learning, Predictive Manufacturing.

M3 - Journal article

JO - Journal of Quality Technology

JF - Journal of Quality Technology

SN - 0022-4065

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