| Title: | Developing a digital twin for human performance assessment in human–machine interaction |
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| Authors: | ID Novak, Erik (Author)
ID Javernik, Aljaž (Author)
ID Palčič, Iztok (Author)
ID Ojsteršek, Robert (Author) |
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| Files: |  https://www.mdpi.com/2075-1702/14/3/346
 machines-14-00346.pdf (1,76 MB)
MD5: 916E9C34200AF7319BCC021FB3F79185
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| Language: | English |
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| Work type: | Article |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: | FS - Faculty of Mechanical Engineering
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| Abstract: | Digital twins are becoming essential tools in smart, human-centric manufacturing, yet validated approaches that integrate real human behavior into digital twin models remain limited. This study develops and experimentally validates a digital twin as a tool for evaluating human performance in balancing human–machine interaction. A physical system comprising a conveyor belt, sensors, and operator-controlled elements was constructed, and a functionally equivalent digital model was created using Arduino IDE and MATLAB/Simulink. The digital twin records and synchronizes key human–machine interaction variables, including response time, assembly time, and execution consistency. Validation was conducted through simulation testing and an experimental study with 18 participants performing repeated assembly cycles. The results show that the developed digital twin accurately replicates the temporal dynamics of the physical process and reliably captures individual human performance patterns. Overall, the study provides a validated methodological framework for human–machine-integrated digital twins and demonstrates their potential for analyzing human–machine interaction, supporting operator training, and adaptive workplace design in line with Industry 5.0 principles. |
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| Keywords: | digital twins, human–machine interaction, smart manufacturing, simulation-based optimization, Industry 5.0 |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Submitted for review: | 19.02.2026 |
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| Article acceptance date: | 18.03.2026 |
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| Publication date: | 19.03.2026 |
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| Publisher: | MDPI |
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| Year of publishing: | 2026 |
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| Number of pages: | 16 str. |
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| Numbering: | Vol. 14, iss. 3, [article no.] 346 |
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| PID: | 20.500.12556/DKUM-97590  |
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| UDC: | 658.5:004.5 |
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| ISSN on article: | 2075-1702 |
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| COBISS.SI-ID: | 272629763 |
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| DOI: | 10.3390/machines14030346 |
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| Publication date in DKUM: | 23.03.2026 |
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| Views: | 157 |
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| Downloads: | 11 |
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| Metadata: |    |
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| Categories: | Misc.
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