Optimizing smart manufacturing system: a digital twin approach utilizing teaching-learning-based optimization

This article introduces an innovative method for optimizing smart manufacturing system (SMS) by combining digital twin technology (DTT) with teaching-learning-based optimization (TBLO). It creates a simulated model of the physical manufacturing environment, enabling real-time monitoring, simulation...

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Bibliographic Details
Published in:Cogent engineering 2024-12, Vol.11 (1)
Main Authors: Deepika Reddy, Gundreddi, Medikondu, Nageswara Rao, Vijaya Kumar, T., Pesaru, Vigneshwar, Anitha Lakshmi, A., Dixit, Saurav, Kumar, Pramod, Alzubaidi, Laith H.
Format: Article
Language:English
Subjects:
Algorithms
Algorithms & Complexity
Artificial Intelligence
Assembly lines
Case studies
Continuous improvement
Cost allocation
Cost control
Decision making
Digital twin
Digital twins
Efficiency
efficiency and Industry
Industry 4.0
Information & Communication Technology (ICT)
infrastructure
innovation
Learning
Logistics
Manufacturing
Mechanical engineering
Optimization
Optimization techniques
Plant layout
Production costs
Production scheduling
Real time
Resource allocation
Resource scheduling
Scheduling
Simulation
smart manufacturing system
Task complexity
Task scheduling
teaching-learning-based optimization
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Summary:This article introduces an innovative method for optimizing smart manufacturing system (SMS) by combining digital twin technology (DTT) with teaching-learning-based optimization (TBLO). It creates a simulated model of the physical manufacturing environment, enabling real-time monitoring, simulation and analysis. By leveraging the TLBO algorithm, the system enhances the decision-making process for complex manufacturing tasks, facilitating continuous improvement and adaptation to dynamic production demands. The proposed framework aims to minimize production costs, reduce downtime and improve overall efficiency by optimizing key parameters such as resource allocation, production scheduling and machine performance. Experimental results demonstrate that the DT-TLBO approach can reduce production costs by up to 20%, decrease downtime by 30% and improve overall system efficiency by 25%. This innovative combination of advanced technologies offers a promising solution for modern manufacturing challenges, paving the way for smarter, more responsive production environments.
ISSN:2331-1916
2331-1916
DOI:10.1080/23311916.2024.2415670