Digital twin for smart manufacturing, A review
Цифровой двойник для интеллектуального производства: обзор
2023-04-01
SCID: 54.1/t4undvw9
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data-driven root-cause analysisdigital twinpredictive maintenanceproduct lifecycle managementsmart manufacturing
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Abstract (AI)
A virtual representation of a physical procedure or product is called digital twin which can enhance efficiency and reduce costs in manufacturing process. Utilizing the digital twin, production teams can examine various data sources and reduce the number of defective items to enhance production efficiency and decrease industrial downtime. Digital Twin can be utilized to visualize the asset, track changes, understand and optimize asset performance throughout the analysis of the product lifecycle. Also, the collected data from digital twin can provide the complete lifecycle of products and processes to optimize workflows of part production, manage supply chain, and manage product quality. The application of digital twin in smart manufacturing can reduce time to market by designing and evaluating the manufacturing processes in virtual environments before manufacture. Comprehensive simulation platforms can be presented using digital twins to simulate and evaluate product performances in terms of analysis and modification of produced parts. Commissioning time of a factory can also be significantly reduced by developing and optimizing the factory layout using the digital twin. Also, the productivity of part manufacturing can be enhanced by providing the predictive maintenance and data-driven root-cause analysis during part production process. In this paper, application of digital twin in smart manufacturing systems is reviewed to analyze and discuss the advantages and challenges of part production modification using the digital twin. So, the research field can advance by reading and evaluating previous papers in order to propose fresh concepts and approaches by using digital twins in smart manufacturing systems.
Key Findings
1
Digital twins provide virtual representations of manufacturing products and processes, enabling simulation, monitoring, and lifecycle optimization.
2
Digital twins support supply-chain management, workflow optimization, product-quality management, and data-driven analysis across the product lifecycle.
3
Predictive maintenance and data-driven root-cause analysis enabled by digital twins can enhance productivity during part manufacturing, although implementation challenges remain.
4
Using data from multiple sources, digital twins can reduce defective production, improve efficiency, and decrease industrial downtime.
5
Virtual design and evaluation of manufacturing processes can reduce time to market and factory commissioning time through optimized layouts.
Research Object
digital twin applications in smart manufacturing systems
Research Subject
advantages and challenges of using digital twins for part-production modification, process optimization, lifecycle management, predictive maintenance, and data-driven root-cause analysis
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2023-04-01
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