TY - JOUR
T1 - ATTACHABLE IOT-BASED DIGITAL TWIN FRAMEWORK SPECIALIZED FOR SME PRODUCTION LINES
AU - Kang, B. G.
AU - Kim, B. S.
N1 - Publisher Copyright:
© 2024, DAAAM International Vienna. All rights reserved.
PY - 2024/9
Y1 - 2024/9
N2 - While large enterprises are actively preparing for digital transformation by leveraging technologies such as digital twins, smaller companies face challenges due to economic constraints and market uncertainties, leading to a relative lack of awareness and readiness. To address this situation, this study proposes a digital twin development framework tailored for small and medium-sized enterprises (SMEs). This framework utilizes attachable IoT devices for real-time collection of manufacturing data and leverages public server systems for data management. Moreover, it enables the refinement and optimization of digital twins by training machine learning models on collected data. Additionally, the framework includes the integration of simulation models and machine learning models for comprehensive digital twin modelling. Finally, the paper suggests a process for applying and validating this framework in real manufacturing companies, demonstrating the effects of digital twin implementation on productivity enhancement in the production lines of two SMEs. (Received in June 2024, accepted in August 2024. This paper was with the authors 2 weeks for 1 revision.).
AB - While large enterprises are actively preparing for digital transformation by leveraging technologies such as digital twins, smaller companies face challenges due to economic constraints and market uncertainties, leading to a relative lack of awareness and readiness. To address this situation, this study proposes a digital twin development framework tailored for small and medium-sized enterprises (SMEs). This framework utilizes attachable IoT devices for real-time collection of manufacturing data and leverages public server systems for data management. Moreover, it enables the refinement and optimization of digital twins by training machine learning models on collected data. Additionally, the framework includes the integration of simulation models and machine learning models for comprehensive digital twin modelling. Finally, the paper suggests a process for applying and validating this framework in real manufacturing companies, demonstrating the effects of digital twin implementation on productivity enhancement in the production lines of two SMEs. (Received in June 2024, accepted in August 2024. This paper was with the authors 2 weeks for 1 revision.).
KW - Digital Twin
KW - IoT Device
KW - Modelling and Simulation
KW - Production Line
KW - Small and Medium-Sized Enterprise
UR - https://www.scopus.com/pages/publications/85203351745
U2 - 10.2507/IJSIMM23-3-694
DO - 10.2507/IJSIMM23-3-694
M3 - Article
AN - SCOPUS:85203351745
SN - 1726-4529
VL - 23
SP - 471
EP - 482
JO - International Journal of Simulation Modelling
JF - International Journal of Simulation Modelling
IS - 3
ER -