Process Data Validation for Manual Assembly Systems used for Highly Variable Products

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Sudhoff, M.; Viehöver, J.; Herzog, M.; Kuhlenkötter, B.: Process Data Validation for Manual Assembly Systems used for Highly Variable Products. In: Herberger, D.; Hübner, M. (Eds.): Proceedings of the Conference on Production Systems and Logistics: CPSL 2022. Hannover : publish-Ing., 2022, S. 181-191. DOI: https://doi.org/10.15488/12165

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Sum total of downloads: 183




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Abstract: 
The production of customized goods is becoming more and more important for industrial companies. The large number of variants resulting from this, up to batch size 1 production, requires a high degree of flexibility. To meet these requirements, manual production processes are frequently still used. This is especially applicable to the area of assembly. Data acquisition is a significant task in manual assembly due to volatile secondary activities and alternative handling operations. The process times to be recorded are also influenced both consciously and unconsciously by the employees. This paper describes an approach for the validation and interpretation of production data of manual assembly systems. Therefore, process data are analysed based on the use case of terminal strip assembly in the learning factory of the Chair of Production Systems at the Ruhr-University Bochum is presented. Here, the validation of the product data from 2021 is carried out by checking the data for normal distribution. This is followed by an analysis of the data with regard to the effects of spikes. Furthermore, the influences of a low data basis, different degrees of standardization and learning effects in the course of production are analysed. Finally, a discussion on the findings and further procedures will take place.
License of this version: CC BY 3.0 DE
Document Type: BookPart
Publishing status: publishedVersion
Issue Date: 2022
Appears in Collections:Proceedings CPSL 2022
Proceedings CPSL 2022

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pos. country downloads
total perc.
1 image of flag of Germany Germany 66 36.07%
2 image of flag of Russian Federation Russian Federation 35 19.13%
3 image of flag of Czech Republic Czech Republic 33 18.03%
4 image of flag of United States United States 13 7.10%
5 image of flag of China China 7 3.83%
6 image of flag of United Kingdom United Kingdom 3 1.64%
7 image of flag of Italy Italy 2 1.09%
8 image of flag of India India 2 1.09%
9 image of flag of Switzerland Switzerland 2 1.09%
10 image of flag of Austria Austria 2 1.09%
    other countries 18 9.84%

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