TUHH Open Research
Help
  • Log In
    New user? Click here to register.Have you forgotten your password?
  • English
  • Deutsch
  • Communities & Collections
  • Publications
  • Research Data
  • People
  • Institutions
  • Projects
  • Statistics
  1. Home
  2. TUHH
  3. Publications
  4. Reducing commissioning efforts for hybrid assembly systems using a data-driven approach
 
Options

Reducing commissioning efforts for hybrid assembly systems using a data-driven approach

Citation Link: https://doi.org/10.15480/882.8684
Publikationstyp
Conference Paper
Date Issued
2023
Sprache
English
Author(s)
Kalscheuer, Florian  orcid-logo
Flugzeug-Produktionstechnik M-23  
Koch, Julian  orcid-logo
Flugzeug-Produktionstechnik M-23  
Schüppstuhl, Thorsten  orcid-logo
Flugzeug-Produktionstechnik M-23  
TORE-DOI
10.15480/882.8684
TORE-URI
https://hdl.handle.net/11420/43585
Journal
Procedia CIRP  
Volume
118
Start Page
935
End Page
939
Citation
Conference 16th CIRP Conference on Intelligent Computation in Manufacturing Engineering - Procedia CIRP 118: 935-939 (2023)
Contribution to Conference
16th CIRP Conference on Intelligent Computation in Manufacturing Engineering, CIRP ICME 2022  
Publisher DOI
10.1016/j.procir.2023.06.161
Scopus ID
2-s2.0-85173578965
Publisher
Elsevier
Is Referenced By
DOI:10.1016/j.procir.2023.06.161
The required flexibility for the assembly of high variant and low volume products such as aircraft components is often met with manual processes. Historically grown and poorly optimized processes are reaching their limits in terms of production rates. The growing demand for these products and increased competitive pressure from low-wage countries are driving companies to increase the level of automation in production, which can be achieved ever more cost-effectively through interconnected resources and digitization. Hybrid assembly systems, consisting of digital assistance systems and automation resources such as projection devices or cobots offer a flexible solution with manageable investment. However, task- and product-specific programming and the commissioning of several sensors and actuators require automation expertise and lead to high costs not only during implementation but also during operation. Furthermore, small and medium-sized enterprises (SMEs) are confronted with the challenge of a low degree of digitalization within their manual processes, high cost for automation expert knowledge, or simply manpower. Therefore, expanding from manual to hybrid assembly systems, several transformation and assignment problems occur and data flow as well as media discontinuity have to be considered. To close this gap, a solution approach consisting of an information model and data transformation pipeline is derived, that can be used as a supplementation for existing planning methodologies. The goal is to reduce the effort for commissioning and operation of automation technology, support engineers on the shopfloor level with missing expert knowledge, and increase transparency in late planning phases during assembly planning. This approach is intended to support the expansion of manual to partially automated assembly systems without the need to adapt the product or plan a new assembly system from scratch.
Subjects
Hybrid assembly
Commissioning
Information modeling
DDC Class
670: Manufacturing
Funding Organisations
Bundesministerium für Wirtschaft und Klimaschutz (BMWK)  
Publication version
publishedVersion
Lizenz
https://creativecommons.org/licenses/by-nc-nd/4.0/
Loading...
Thumbnail Image
Name

1-s2.0-S2212827123003888-main.pdf

Type

Main Article

Size

543.34 KB

Format

Adobe PDF

TUHH
Weiterführende Links
  • Contact
  • Send Feedback
  • Cookie settings
  • Privacy policy
  • Impress
DSpace Software

Built with DSpace-CRIS software - Extension maintained and optimized by 4Science
Design by effective webwork GmbH

  • Deutsche NationalbibliothekDeutsche Nationalbibliothek
  • ORCiD Member OrganizationORCiD Member Organization
  • DataCiteDataCite
  • Re3DataRe3Data
  • OpenDOAROpenDOAR
  • OpenAireOpenAire
  • BASE Bielefeld Academic Search EngineBASE Bielefeld Academic Search Engine
Feedback