An optimization-centric review on integrating artificial intelligence and digital twin technologies in manufacturing

被引：3

作者：

Karkaria, Vispi ^{[1
]}

Tsai, Ying-Kuan ^{[1
]}

Chen, Yi-Ping ^{[1
]}

Chen, Wei ^{[1
]}

机构：

[1] Northwestern Univ, Dept Mech Engn, Evanston, IL 60208 USA

来源：

ENGINEERING OPTIMIZATION | 2025年 / 57卷 / 01期

关键词：

Digital twins; optimization; artificial intelligence; manufacturing systems and machine learning; MODEL-PREDICTIVE CONTROL; MULTIDISCIPLINARY DESIGN OPTIMIZATION; SIMULATED ANNEALING ALGORITHM; RECURRENT NEURAL-NETWORKS; OF-THE-ART; SYSTEM-IDENTIFICATION; BIG DATA; TOPOLOGY OPTIMIZATION; DYNAMICAL-SYSTEMS; DECISION-MAKING;

D O I：

10.1080/0305215X.2024.2434201

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

This article reviews optimization methods that enhance adaptability, efficiency and decision making in modern manufacturing, emphasizing the transformative role of artificial intelligence (AI) and digital twin technologies. By integrating AI and machine learning algorithms within digital twin frameworks, manufacturers can facilitate real-time monitoring, quality control and dynamic process adjustments. This synergy not only boosts operational efficiency but also enables precise modelling, offering predictive insights for strategic planning and innovation. The combination of digital twins and optimization techniques supports resource optimization, balancing competing objectives and driving continuous process improvements. With both offline and online optimization approaches, digital twins enable efficient production adjustments while ensuring long-term performance and scalability. Ultimately, this review highlights digital twins as foundational technologies for smart, sustainable manufacturing, incorporating advanced optimization strategies to enhance adaptability and operational resilience. This positions optimization algorithms and digital twins as key drivers in the future of intelligent production systems.

引用

页码：161 / 207

页数：47

共 319 条

[1]

Abbas A N., 2022, ARTICLES, DOI [https://doi.org/10.3850/981-973-0000-00-0output, DOI 10.3850/981-973-0000-00-0OUTPUT]

[2] A Survey of Parallel Implementations for Model Predictive Control [J].

Abughalieh, Karam M. ;

Alawneh, Shadi G. .

IEEE ACCESS, 2019, 7 :34348-34360

[3] Gradient-based optimizer: A new metaheuristic optimization algorithm [J].

Ahmadianfar, Iman ;

Bozorg-Haddad, Omid ;

Chu, Xuefeng .

INFORMATION SCIENCES, 2020, 540 :131-159

[4]

Ahn K., 2023, P 5 ANN LEARN DYN CO, P1493

[5] System Identification of an Airship using Trust Region Reflective Least Squares Algorithm [J].

Ahsan, Mansoor ;

Choudhry, Mohammad Ahmad .

INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS, 2017, 15 (03) :1384-1393

[6] Digital Twin: A Comprehensive Survey of Security Threats [J].

Alcaraz, Cristina ;

Lopez, Javier .

IEEE COMMUNICATIONS SURVEYS AND TUTORIALS, 2022, 24 (03) :1475-1503

[7]

Alessio A, 2009, LECT NOTES CONTR INF, V384, P345, DOI 10.1007/978-3-642-01094-1_29

[8] A trust-region framework for managing the use of approximation models in optimization [J].

Alexandrov, NM ;

Dennis, JE ;

Lewis, RM ;

Torczon, V .

STRUCTURAL OPTIMIZATION, 1998, 15 (01) :16-23

[9] Digital twin-driven supervised machine learning for the development of artificial intelligence applications in manufacturing [J].

Alexopoulos, Kosmas ;

Nikolakis, Nikolaos ;

Chryssolouris, George .

INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING, 2020, 33 (05) :429-439

[10] Using an autoencoder in the design of an anomaly detector for smart manufacturing [J].

Alfeo, Antonio L. ;

Cimino, Mario G. C. A. ;

Manco, Giuseppe ;

Ritacco, Ettore ;

Vaglini, Gigliola .

PATTERN RECOGNITION LETTERS, 2020, 136 :272-278

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