by Ouijdane Guiza, Christoph Mayr-Dorn, Georg Weichhart, Michael Mayrhofer, Bahman Bahman Zangi, Alexander Egyed, Björn Fanta, Martin Gieler
Abstract:
As manufacturing companies move towards producing highly customizable products in small lot sizes, assembly workers remain an integral part of production systems. However, with workers in the loop, it is necessary to monitor the production process for timely detection of deviations and timely provisioning of worker assistance. Grounded in an industrial case study describing the assembly of construction vehicles, we outline a generic heuristic-based approach for monitoring progress in human-intensive assembly systems. Specifically, we highlight the challenges in dealing with uncertainty stemming from the limitations in accurately, timely, and completely observing human physical assembly steps. We discuss a motivating example to showcase these challenges and present a set of heuristics that manages to accurately infer assembly progress from indirect and incomplete observations of deviating worker behavior. Validated against ground truth obtained from a real industrial assembly line, on average our approach correctly estimates completion times for steps that are associated with shopfloor observations within 14 seconds or less of their true value.
Reference:
Monitoring of Human-Intensive Assembly Processes Based on Incomplete and Indirect Shopfloor Observations (Ouijdane Guiza, Christoph Mayr-Dorn, Georg Weichhart, Michael Mayrhofer, Bahman Bahman Zangi, Alexander Egyed, Björn Fanta, Martin Gieler), In 19th IEEE International Conference on Industrial Informatics, INDIN 2021, Palma de Mallorca, Spain, July 21-23, 2021, IEEE, 2021.
Bibtex Entry:
@Conference{Guiza2021b,
author = {Ouijdane Guiza and Christoph Mayr-Dorn and Georg Weichhart and Michael Mayrhofer and Bahman Bahman Zangi and Alexander Egyed and Björn Fanta and Martin Gieler},
booktitle = {19th {IEEE} International Conference on Industrial Informatics, {INDIN} 2021, Palma de Mallorca, Spain, July 21-23, 2021},
title = {Monitoring of Human-Intensive Assembly Processes Based on Incomplete and Indirect Shopfloor Observations},
year = {2021},
pages = {1--8},
publisher = {{IEEE}},
abstract = {As manufacturing companies move towards producing highly customizable products in small lot sizes, assembly workers remain an integral part of production systems. However, with workers in the loop, it is necessary to monitor the production process for timely detection of deviations and timely provisioning of worker assistance. Grounded in an industrial case study describing the assembly of construction vehicles, we outline a generic heuristic-based approach for monitoring progress in human-intensive assembly systems. Specifically, we highlight the challenges in dealing with uncertainty stemming from the limitations in accurately, timely, and completely observing human physical assembly steps. We discuss a motivating example to showcase these challenges and present a set of heuristics that manages to accurately infer assembly progress from indirect and incomplete observations of deviating worker behavior. Validated against ground truth obtained from a real industrial assembly line, on average our approach correctly estimates completion times for steps that are associated with shopfloor observations within 14 seconds or less of their true value.},
bibsource = {dblp computer science bibliography, https://dblp.org},
biburl = {https://dblp.org/rec/conf/indin/GuizaMWMZEFG21a.bib},
doi = {10.1109/INDIN45523.2021.9557551},
file = {:C\:/OwnCloud/PUBLIC~1/CONFER~1/INDIN2~2/MONITO~1.PDF:PDF},
keywords = {Pro2Future},
timestamp = {Mon, 18 Oct 2021 17:08:58 +0200},
url = {https://doi.org/10.1109/INDIN45523.2021.9557551},
}