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Activity recognition in manual manufacturing: Detecting screwing processes from sensor data

 
: Günther, Lisa; Kärcher, Susann; Bauernhansl, Thomas

:
Volltext urn:nbn:de:0011-n-5525331 (714 KByte PDF)
MD5 Fingerprint: 75d206000aaa69d82e34bd7c144b574c
(CC) by-nc-nd
Erstellt am: 30.7.2019


Procedia CIRP 81 (2019), S.1177-1182
ISSN: 2212-8271
Conference on Manufacturing Systems (CMS) <52, 2019, Ljubljana>
Englisch
Zeitschriftenaufsatz, Konferenzbeitrag, Elektronische Publikation
Fraunhofer IPA ()
manuelle Fertigung; maschinelles Lernen; deep learning; Sensor; Fertigungsplanung

Abstract
Knowledge about the duration of manufacturing processes and operation times is essential for production planning and control. But data acquisition is often difficult and especially challenging if production requires manual activities. This paper presents different data analysis and machine learning approaches to detect manual manufacturing processes from sensor data. As human activity recognition approaches are not necessarily applicable in industrial environments, all sensors are attached to tools, in this case screwdrivers. A dataset covering different tool movements, sensor types and mounting options is created and analyzed. The results are evaluated in terms of feasibility of the approach.

: http://publica.fraunhofer.de/dokumente/N-552533.html