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Industry 4.0: Mining physical defects in production of surface-mount devices

 
: Tavakolizadeh, Farshid; Carvajal Soto, José Ángel; Gyulai, Dávid; Beecks, Christian

:
Volltext urn:nbn:de:0011-n-4974194 (427 KByte PDF)
MD5 Fingerprint: fa0a6400b915f9a5265ef9e7a41e1203
Erstellt am: 28.6.2018


Perner, P.:
Advances in Data Mining. Poster Proceedings : 17th Industrial Conference, ICDM 2017, New York, USA, July 2017
Leipzig: IBaI Publishing, 2017
ISBN: 978-3-942952-50-7
S.146-151
Industrial Conference on Data Mining (ICDM) <17, 2017, New York/NY>
European Commission EC
H2020; 723145; COMPOSITION
European Commission EC
H2020; 691829; EXCELL
Englisch
Konferenzbeitrag, Elektronische Publikation
Fraunhofer FIT ()
industry 4.0; surface-mount technology; data mining; classification

Abstract
With the advent of Industry 4.0, production processes have been endowed with intelligent cyber-physical systems generating massive amounts of streaming sensor data. Internet of Things technologies have enabled capturing, managing, and processing production data at a large scale in order to utilize this data as an asset for the optimization of production processes. In this work, we focus on the automatic detection of physical defects in the production of surfacemount devices. We show how to build a classification model based on random forests that efficiently detects defect products with a high degree of precision. In fact, the results of our preliminary experimental analysis indicate that our approach is able to correctly determine defects in a simulated production environment of surface-mount devices with a MCC score of 0.96. We investigate the feasibility of utilizing this approach in realistic settings. We believe that our approach will help to advance the production of surface-mount devices.

: http://publica.fraunhofer.de/dokumente/N-497419.html