Procedia CIRP, Journal Year: 2024, Volume and Issue: 130, P. 802 - 809
Published: Jan. 1, 2024
Language: Английский
Electronics, Journal Year: 2025, Volume and Issue: 14(4), P. 646 - 646
Published: Feb. 7, 2025
Digital twins (DTs) represent a transformative technology in manufacturing, facilitating significant advancements monitoring, simulation, and optimization. This paper offers an extensive bibliographic review of AI-Based DT applications, categorized into three principal dimensions: operator, process, product. The operator dimension focuses on enhancing safety ergonomics through intelligent assistance, utilizing real-time monitoring artificial intelligence, notably human–robot collaboration contexts. process application concerns itself with optimizing production flows, identifying bottlenecks, dynamically reconfiguring systems predictive models simulations. Lastly, the product emphasizes applications focused improvements design quality, employing lifecycle historical data to satisfy evolving market requirements. categorization provides structured framework for analyzing specific capabilities trends DTs, while also knowledge gaps contemporary research. highlights key challenges technological interoperability, integration, high implementation costs emphasizing how digital twins, supported by AI, can drive transition toward sustainable, human-centered manufacturing line Industry 5.0. findings provide valuable insights advancing state art exploring future opportunities twin applications.
Language: Английский
Citations
3
Robotics and Computer-Integrated Manufacturing, Journal Year: 2024, Volume and Issue: 89, P. 102778 - 102778
Published: May 4, 2024
Language: Английский
Citations
16
Journal of Industrial Information Integration, Journal Year: 2023, Volume and Issue: 35, P. 100492 - 100492
Published: July 4, 2023
Language: Английский
Citations
19
Procedia Computer Science, Journal Year: 2023, Volume and Issue: 221, P. 1216 - 1225
Published: Jan. 1, 2023
The advent of new-generation information and communication technologies, such as Generative AI, Internet Things (IoT), big data analytics, Blockchain technology, artificial intelligence (AI), has led to the emergence era in recent years. Digital twin emerged one most active components smart manufacturing, garnering significant attention from enterprises, research institutes, researchers. By creating a digital twin, manufacturers can simulate different scenarios test various configurations without disrupting actual production process. This allows for more efficient testing optimization processes, well improved quality control predictive maintenance. Overall, twins are an important tool that help improve efficiency reduce costs while ensuring high-quality output. In this paper, after reviewing literature on subject, article, by literature, we presented framework which includes Optimization, Predictive Maintenance, Quality Control, Design, Simulation, be good guide future studies.
Language: Английский
Citations
15
Decision Analytics Journal, Journal Year: 2024, Volume and Issue: 12, P. 100502 - 100502
Published: July 9, 2024
In recent years, Digital Twins (DTs) implementation has significantly impacted various sectors like industry, healthcare, engineering, and technology. However, the examination of these areas concerning pandemic management is still in its early stages. To bridge this gap, a systematic literature review was conducted here spanning from 2017 to March 2024, with specific focus on COVID-19-related issues 2020 2024. Employing five-step filtering process, nearly 10,000 articles were initially identified based search strings. Subsequently, 297 publications selected examined across pre-pandemic, pandemic, post-pandemic phases discern emerging patterns, limitations, future research directions. Drawing insights, concept for 'Digital-Twin-based Smart Pandemic City' proposed, aiming ensure contemporary amenities while preparing potential pandemics by leveraging advanced cloud storage blockchain technology secure data aggregation. Anticipated challenges that may arise implementing model also explored study.
Language: Английский
Citations
6
Computers in Industry, Journal Year: 2023, Volume and Issue: 152, P. 104006 - 104006
Published: Aug. 12, 2023
Language: Английский
Citations
12
The International Journal of Advanced Manufacturing Technology, Journal Year: 2023, Volume and Issue: 128(1-2), P. 405 - 421
Published: July 12, 2023
Language: Английский
Citations
11
Advanced Engineering Informatics, Journal Year: 2025, Volume and Issue: 65, P. 103102 - 103102
Published: Jan. 9, 2025
Language: Английский
Citations
0
Robotics and Computer-Integrated Manufacturing, Journal Year: 2025, Volume and Issue: 96, P. 103035 - 103035
Published: May 8, 2025
Language: Английский
Citations
0
Procedia Computer Science, Journal Year: 2024, Volume and Issue: 232, P. 934 - 945
Published: Jan. 1, 2024
Swarm Production is a novel paradigm promoting high degree of flexibility in material flow and reconfigurability shop floor layout, enabling high-mix production manufacturing through the induction autonomous robots. The conceptual description promises leap within smart Industry 4.0 domain. Implementing this theoretical concept becomes crucial to self-organising domain gradually learning relevant technological stack, optimisation methods, objective functions, constraints needed for Production. This research paper addresses challenges simulation mobile robots environment concludes with an exemplified case simulation. A testbed visually demonstrates planning, scheduling control systems, providing their feasibility reliability.
Language: Английский
Citations
2