The impact of digital twins on the evolution of intelligent manufacturing and Industry 4.0

Attaran, Mohsen; Attaran, Sharmin; Çelik, Bilge Gökhan

doi:10.1007/s43674-023-00058-y

Cited by 37 publications

(13 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The spike in the number of papers in this stage was mainly driven by the COVID-19 pandemic, where industries realized that resiliency was important to manage operating cost and profit for sustainability during an emergency [142][143][144]. Digital twin was also used to perform impact analysis for the food retail supply chain to help the companies cope with the adverse impacts of COVID-19 [145].…”

Section: Prosperity Stage (2021-present)mentioning

confidence: 99%

A Bibliometric Analysis of Digital Twin in the Supply Chain

Lam,

Lee

2023

Mathematics

View full text Add to dashboard Cite

Digital twin is the digital representation of an entity, and it drives Industry 4.0. This paper presents a bibliometric analysis of digital twin in the supply chain to help researchers, industry practitioners, and academics to understand the trend, development, and focus of the areas of digital twin in the supply chain. This paper found several key clusters of research, including the designing of a digital twin model, integration of a digital twin model, application of digital twin in quality control, and digital twin in digitalization. In the embryonic stage of research, digital twin was tested in the production line with limited optimization. In the development stage, the importance of digital twin in Industry 4.0 was observed, as big data, machine learning, Industrial Internet of Things, blockchain, edge computing, and cloud-based systems complemented digital twin models. Digital twin was applied to improve sustainability in manufacturing and production logistics. In the current prosperity stage with high annual publications, the recent trends of this topic focus on the integration of deep learning, data models, and artificial intelligence for digitalization. This bibliometric analysis also found that the COVID-19 pandemic drove the start of the prosperity stage of digital twin research in the supply chain. Researchers in this field are slowly moving towards applying digital twin for human-centric systems and mass personalization to prepare to transit to Industry 5.0.

show abstract

Section: Prosperity Stage (2021-present)mentioning

confidence: 99%

A Bibliometric Analysis of Digital Twin in the Supply Chain

Lam,

Lee

2023

Mathematics

View full text Add to dashboard Cite

show abstract

“…DT technology continues to evolve in the construction sector, relying on various base components, such as technologies, maturity levels, data layers, and functionalities, depending on the specific application. In their work, Attaran et al [1,25] highlight four primary technologies-Internet of Things (IoT), artificial intelligence (AI), cloud computing, and extended reality (XR)-used for the collection of real-time data, information extraction, and the development of digital representations. The DT relies on data from physical sensors provided by the IoT.…”

Section: Components Of Digital Twinsmentioning

confidence: 99%

“…IoT facilitates the connection of a vast network of objects, people, and their interactions. By 2027, more than 90% of IoT platforms are projected to possess digital twinning capabilities, collecting data from real-world objects to create digital replicas for analysis, manipulation, and optimization [1,25]. The real-time virtual representations are continuously updated with data.…”

Section: Internet Of Things (Iot)mentioning

confidence: 99%

Delving into the Digital Twin Developments and Applications in the Construction Industry: A PRISMA Approach

Afzal,

Li,

Shoaib

et al. 2023

Sustainability

View full text Add to dashboard Cite

Construction 4.0 is witnessing exponential growth in digital twin (DT) technology developments and applications, revolutionizing the adoption of building information modelling (BIM) and other emerging technologies used throughout the built environment lifecycle. BIM provides technologies, procedures, and data schemas representing building components and systems. At the same time, the DT enhances this with real-time data for integrating cyber-physical systems, enabling live asset monitoring and better decision making. Despite being in the early stages of development, DT applications have rapidly progressed in the AEC sector, resulting in a diverse literature landscape due to the various technologies and parameters involved in fully developing the DT technology. The intricate complexities inherent in digital twin advancements have confused professionals and researchers. This confusion arises from the nuanced distinctions between the two technologies, i.e., BIM and DT, causing a convergence that hinders realizing their potential. To address this confusion and lead to a swift development of DT technology, this study provides a holistic review of the existing research focusing on the critical components responsible for developing the applications of DT technology in the construction industry. It highlights five crucial elements: technologies, maturity levels, data layers, enablers, and functionalities. Additionally, it identifies research gaps and proposes future avenues for streamlined DT developments and applications in the AEC sector. Future researchers and practitioners can target data integrity, integration and transmission, bi-directional interoperability, non-technical factors, and data security to achieve mature digital twin applications for AEC practices. This study highlights the growing significance of DTs in construction and provides a foundation for further advancements in this field to harness its potential to transform built environment practices. It also pinpoints the latest developments in AI, namely the large language model (LLM) and retrieval-augmented generation (RAG)’s implications for DT education, policies, and the construction industry’s practices.

show abstract

“…Advanced manufacturing and digital twin technologies offer new opportunities to improve the efficiency, reliability, and safety of nuclear energy production (Grieves, 2015;Kochunas and Huan, 2021;Pal et al, 2022). To meet the industry's evolving needs, advanced manufacturing techniques and digital twin technology have emerged as transformative tools (Ostaševičius, 2022;Sleiti et al, 2022;Attaran et al, 2023). This section provides an overview of the significance and potential of these technologies in the context of nuclear energy.…”

Section: Introductionmentioning

confidence: 99%

Advanced manufacturing and digital twin technology for nuclear energy*

Mondal,

Martinez,

Jain

2024

Front. Energy Res.

View full text Add to dashboard Cite

Advanced manufacturing techniques and digital twin technology are rapidly transforming the nuclear industry, offering the potential to enhance productivity, safety, and cost-effectiveness. Customized parts are being produced using additive manufacturing, automation, and robotics, while digital twin technology enables the virtual modeling and optimization of complex systems. These advanced technologies can significantly improve operational efficiency, predict system behavior, and optimize maintenance schedules in the nuclear energy sector, leading to heightened safety and reduced downtime. However, the nuclear industry demands the highest levels of safety and security, as well as intricate manufacturing processes and operations. Thus, challenges such as data management and cybersecurity must be addressed to fully realize the potential of advanced manufacturing techniques and digital twin technology in the nuclear industry. This comprehensive review highlights the critical role of digital twin technology with advanced manufacturing toward nuclear energy to improve performance, minimize downtime, and heighten safety, ultimately contributing to the global energy mix by providing dependable and low-carbon electricity.

show abstract

The impact of digital twins on the evolution of intelligent manufacturing and Industry 4.0

Cited by 37 publications

References 56 publications

A Bibliometric Analysis of Digital Twin in the Supply Chain

A Bibliometric Analysis of Digital Twin in the Supply Chain

Delving into the Digital Twin Developments and Applications in the Construction Industry: A PRISMA Approach

Advanced manufacturing and digital twin technology for nuclear energy*

Contact Info

Product

Resources

About