Personalization of AI-Based Digital Twins to Optimize Adaptation in Industrial Design and Manufacturing—Review
The growing scale of big data and artificial intelligence (AI)-based models has heightened the urgency of developing real-time digital twins (DTs), particularly those capable of simulating personalized behavior in dynamic environments. In this study, we examine the personalization of AI-based digita...
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| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/15/8525 |
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| author | Izabela Rojek Dariusz Mikołajewski Ewa Dostatni Jan Cybulski Mirosław Kozielski |
| author_facet | Izabela Rojek Dariusz Mikołajewski Ewa Dostatni Jan Cybulski Mirosław Kozielski |
| author_sort | Izabela Rojek |
| collection | DOAJ |
| description | The growing scale of big data and artificial intelligence (AI)-based models has heightened the urgency of developing real-time digital twins (DTs), particularly those capable of simulating personalized behavior in dynamic environments. In this study, we examine the personalization of AI-based digital twins (DTs), with a focus on overcoming computational latencies that hinder real-time responses—especially in complex, large-scale systems and networks. We use bibliometric analysis to map current trends, prevailing themes, and technical challenges in this field. The key findings highlight the growing emphasis on scalable model architectures, multimodal data integration, and the use of high-performance computing platforms. While existing research has focused on model decomposition, structural optimization, and algorithmic integration, there remains a need for fast DT platforms that support diverse user requirements. This review synthesizes these insights to outline new directions for accelerating adaptation and enhancing personalization. By providing a structured overview of the current research landscape, this study contributes to a better understanding of how AI and edge computing can drive the development of the next generation of real-time personalized DTs. |
| format | Article |
| id | doaj-art-3cf727e73a19440391aecf3eac97d3d3 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-3cf727e73a19440391aecf3eac97d3d32025-08-20T03:36:34ZengMDPI AGApplied Sciences2076-34172025-07-011515852510.3390/app15158525Personalization of AI-Based Digital Twins to Optimize Adaptation in Industrial Design and Manufacturing—ReviewIzabela Rojek0Dariusz Mikołajewski1Ewa Dostatni2Jan Cybulski3Mirosław Kozielski4Faculty of Computer Science, Kazimierz Wielki University, 30 Chodkiewicza St., 85-064 Bydgoszcz, PolandFaculty of Computer Science, Kazimierz Wielki University, 30 Chodkiewicza St., 85-064 Bydgoszcz, PolandFaculty of Mechanical Engineering, Poznań University of Technology, Marii Skłodowskiej-Curie 5, 60-965 Poznan, PolandFaculty of Computer Science, Kazimierz Wielki University, 30 Chodkiewicza St., 85-064 Bydgoszcz, PolandFaculty of Computer Science, Kazimierz Wielki University, 30 Chodkiewicza St., 85-064 Bydgoszcz, PolandThe growing scale of big data and artificial intelligence (AI)-based models has heightened the urgency of developing real-time digital twins (DTs), particularly those capable of simulating personalized behavior in dynamic environments. In this study, we examine the personalization of AI-based digital twins (DTs), with a focus on overcoming computational latencies that hinder real-time responses—especially in complex, large-scale systems and networks. We use bibliometric analysis to map current trends, prevailing themes, and technical challenges in this field. The key findings highlight the growing emphasis on scalable model architectures, multimodal data integration, and the use of high-performance computing platforms. While existing research has focused on model decomposition, structural optimization, and algorithmic integration, there remains a need for fast DT platforms that support diverse user requirements. This review synthesizes these insights to outline new directions for accelerating adaptation and enhancing personalization. By providing a structured overview of the current research landscape, this study contributes to a better understanding of how AI and edge computing can drive the development of the next generation of real-time personalized DTs.https://www.mdpi.com/2076-3417/15/15/8525digital transformationdigital twinartificial intelligencemachine learningIndustry 4.0Industry 5.0 |
| spellingShingle | Izabela Rojek Dariusz Mikołajewski Ewa Dostatni Jan Cybulski Mirosław Kozielski Personalization of AI-Based Digital Twins to Optimize Adaptation in Industrial Design and Manufacturing—Review Applied Sciences digital transformation digital twin artificial intelligence machine learning Industry 4.0 Industry 5.0 |
| title | Personalization of AI-Based Digital Twins to Optimize Adaptation in Industrial Design and Manufacturing—Review |
| title_full | Personalization of AI-Based Digital Twins to Optimize Adaptation in Industrial Design and Manufacturing—Review |
| title_fullStr | Personalization of AI-Based Digital Twins to Optimize Adaptation in Industrial Design and Manufacturing—Review |
| title_full_unstemmed | Personalization of AI-Based Digital Twins to Optimize Adaptation in Industrial Design and Manufacturing—Review |
| title_short | Personalization of AI-Based Digital Twins to Optimize Adaptation in Industrial Design and Manufacturing—Review |
| title_sort | personalization of ai based digital twins to optimize adaptation in industrial design and manufacturing review |
| topic | digital transformation digital twin artificial intelligence machine learning Industry 4.0 Industry 5.0 |
| url | https://www.mdpi.com/2076-3417/15/15/8525 |
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