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Title:Optimizing smart manufacturing systems using digital twin
Authors:ID Ojsteršek, Robert (Author)
ID Javernik, Aljaž (Author)
ID Buchmeister, Borut (Author)
Files:.pdf APEM18-4_475-485.pdf (1,28 MB)
MD5: DA91EF25DC6CAD0BDD3E6671131509F2
 
URL https://apem-journal.org/Archives/2023/Abstract-APEM18-4_475-485.html
 
Language:English
Work type:Article
Typology:1.01 - Original Scientific Article
Organization:FS - Faculty of Mechanical Engineering
Abstract:Presented paper investigates the application of digital twins for the optimisation of intelligent manufacturing systems and focuses on the comparison between simulation modelling results and real-world production conditions. A digital twin was created in the Simio software environment using a data-driven simulation model derived from a real-world production system. Running the digital twin in real time, which was displayed graphically, facilitated the analysis of key parameters, including the number of finished products, average flow time, workstation utilization and product quality. The discrepancies were attributed to the use of random distributions of input data in the dynamic digital twin, as opposed to the long-term measurements and averages in the real-world system. Despite the limitations in the case study, the results underline the financial justification and predictive capabilities of digital twins for optimising production systems. Real-time operation enables continuous evaluation and tracking of parameters and offers high benefits for intelligent production systems. The study emphasises the importance of accurate selection of input data and warns that even small deviations can lead to inaccurate results. Finally, the paper high-lights the role of digital twins in optimising production systems and argues for careful consideration of input data. It highlights the importance of analysing real-world production systems and creating efficient simulation models as a basis for digital twin solutions. The results encourage extending the research to different types of production, from job shop to mass production, in order to obtain a comprehensive optimisation perspective.
Keywords:smart manufacturing, digital twin, optimisation, simulation modelling, Simio, case study
Publication status:Published
Publication version:Version of Record
Submitted for review:19.07.2023
Article acceptance date:20.12.2023
Publication date:28.12.2023
Publisher:Chair of Production Engineering (CPE), University of Maribor, Faculty of Mechanical Engineering
Year of publishing:2023
Number of pages:str. 475-485
Numbering:Vol. 18, no. 4
PID:20.500.12556/DKUM-87708 New window
UDC:658.5:004.94
ISSN on article:1854-6250
COBISS.SI-ID:182000131 New window
DOI:10.14743/apem2023.4.486 New window
Publication date in DKUM:25.03.2024
Views:277
Downloads:24
Metadata:XML DC-XML DC-RDF
Categories:Misc.
:
OJSTERŠEK, Robert, JAVERNIK, Aljaž and BUCHMEISTER, Borut, 2023, Optimizing smart manufacturing systems using digital twin. Advances in production engineering & management [online]. 2023. Vol. 18, no. 4, p. 475–485. [Accessed 14 April 2025]. DOI 10.14743/apem2023.4.486. Retrieved from: https://dk.um.si/IzpisGradiva.php?lang=eng&id=87708
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Record is a part of a journal

Title:Advances in production engineering & management
Shortened title:Adv produc engineer manag
Publisher:Fakulteta za strojništvo, Inštitut za proizvodno strojništvo
ISSN:1854-6250
COBISS.SI-ID:229859072 New window

Document is financed by a project

Funder:ARIS - Slovenian Research and Innovation Agency
Project number:P2-0190
Name:Napredni koncepti menedžmenta proizvodnje in dimenzionalnega meroslovja

Licences

License:CC BY 4.0, Creative Commons Attribution 4.0 International
Link:http://creativecommons.org/licenses/by/4.0/
Description:This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

Secondary language

Language:Slovenian
Keywords:pametna proizvodnja, simulacijsko modeliranje, digitalni dvojček, optimizacija, študija primera


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