Zimmer_PID6018741.pdf (871.03 kB)
Understanding human decision-making during production ramp-up using natural language processing
conference contribution
posted on 2019-06-13, 12:56 authored by Melanie Zimmer, Ali Al-Yacoub, Pedro FerreiraPedro Ferreira, Niels LohseRamping up a manufacturing system from being
just assembled to full-volume production capacity is a time
consuming and error-prone task. The full behaviour of a system
is difficult to predict in advance and disruptions that need to be
resolved until the required performance targets are reached
occur often. Information about the experienced faults and issues
might be recorded, but usually, no record of decisions
concerning necessary physical and process adjustments are
kept. Having these data could help to uncover significant
insights into the ramp-up process that could reduce the effort
needed to bring the system to its mandatory state. This paper
proposes Natural Language Processing (NLP) to interpret
human operator comments collected during ramp-up.
Recurring patterns in their feedback could be used to gain a
deeper understanding of the cause and effect relationship
between the system state and the corrective action that an
operator applied. A manual dispensing experiment was
conducted where human assessments in form of unstructured
free-form text were gathered. These data have been used as an
input for initial NLP analysis and preliminary results using the
NLTK library are presented. Outcomes show first insights into
the topics participants considered and lead to valuable
knowledge to learn from this experience for the future.
Funding
The authors gratefully acknowledge the financial support of the Engineering and Physical Sciences Research Council (EPSRC) Centre for Doctoral Training in Embedded Intelligence under grant reference EP/L014998/1. The research leading to these results has also received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 680735, project openMOS (Open Dynamic Manufacturing Operating System for Smart Plug-and-Produce Automation Components).
History
School
- Science
Department
- Mathematical Sciences
Published in
2019 IEEE 17th International Conference on Industrial Informatics (INDIN)Pages
337 - 342Citation
ZIMMER, M. ... et al., 2019. Understanding human decision-making during production ramp-up using natural language processing. Presented at the 17th International Conference on Industrial Informatics (IEEE-INDIN 2019), Helsinki, Finland, 22-25 July 2019, pp.337-342.Publisher
© IEEEVersion
- AM (Accepted Manuscript)
Publisher statement
© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Acceptance date
2019-05-03Publication date
2020-01-30Copyright date
2019ISBN
9781728129273eISSN
2378-363XPublisher version
Language
- en