Publikationer inom industriella produktionssystem
Här visas de 50 senaste publikationerna från enheten för industriella produktionssystem.
[1]
Q. Qin et al.,
"Robot digital twin systems in manufacturing : Technologies, applications, trends and challenges,"
Robotics and Computer-Integrated Manufacturing, vol. 97, 2026.
[2]
X. Liu et al.,
"A cloud manufacturing service composition optimization method for fuzzy demands based on improved NSGA-III algorithm,"
Robotics and Computer-Integrated Manufacturing, vol. 97, 2026.
[3]
D. Wu et al.,
"Empowering natural human–robot collaboration through multimodal language models and spatial intelligence: Pathways and perspectives,"
Robotics and Computer-Integrated Manufacturing, vol. 97, 2026.
[4]
B. Zi et al.,
"Coating defect detection in intelligent manufacturing: Advances, challenges, and future trends,"
Robotics and Computer-Integrated Manufacturing, vol. 97, 2026.
[5]
R. Zhong et al.,
"A two-stage framework for learning human-to-robot object handover policy from 4D spatiotemporal flow,"
Robotics and Computer-Integrated Manufacturing, vol. 98, 2026.
[6]
M. Sun et al.,
"Out-of-order execution enabled deep reinforcement learning for dynamic additive manufacturing scheduling,"
Robotics and Computer-Integrated Manufacturing, vol. 91, 2025.
[7]
Y. Qin et al.,
"A tool wear monitoring method based on data-driven and physical output,"
Robotics and Computer-Integrated Manufacturing, vol. 91, 2025.
[8]
X. Wang et al.,
"Dynamic multi-tour order picking in an automotive-part warehouse based on attention-aware deep reinforcement learning,"
Robotics and Computer-Integrated Manufacturing, vol. 94, s. 102959-102959, 2025.
[9]
Q. Wang et al.,
"A phased robotic assembly policy based on a PL-LSTM-SAC algorithm,"
Journal of manufacturing systems, vol. 78, s. 351-369, 2025.
[10]
B. Zhang et al.,
"An imbalanced data learning approach for tool wear monitoring based on data augmentation,"
Journal of Intelligent Manufacturing, vol. 36, no. 1, s. 399-420, 2025.
[11]
J. Leng et al.,
"Federated learning-empowered smart manufacturing and product lifecycle management : A review,"
Advanced Engineering Informatics, vol. 65, 2025.
[12]
B. Wang et al.,
"Context-aware AR adaptive information push for product assembly: Aligning information load with human cognitive abilities,"
Advanced Engineering Informatics, vol. 64, 2025.
[13]
J. Leng et al.,
"Review of manufacturing system design in the interplay of Industry 4.0 and Industry 5.0 (Part II) : Design processes and enablers,"
Journal of manufacturing systems, vol. 79, s. 528-562, 2025.
[14]
H. U. Rehman et al.,
"Intelligent configuration management in modular production systems : Integrating operational semantics with knowledge graphs,"
Journal of manufacturing systems, vol. 80, s. 610-625, 2025.
[15]
T. Li et al.,
"Online inverse solution for deep learning-based prognostics,"
i Structural Health Monitoring - The 10th Asia-Pacific Workshop on Structural Health Monitoring, 10APWSHM 2024, 2025, s. 119-126.
[16]
Q. Meng et al.,
"Grinding Chatter Online Monitoring Based on Multi-Sensor Fusion Information and Hybrid Deep Neural Network,"
IEEE Transactions on Industrial Informatics, 2025.
[17]
Y. Liu et al.,
"Fusing human action recognition and object detection for human-robot collaborative assembly,"
International Journal of Modeling, Simulation, and Scientific Computing, vol. 16, no. 03, 2025.
[18]
Z. Liu, F. Davoli och D. Borsatti,
"Industrial Internet of Things (IIoT) : Trends and Technologies,"
Future Internet, vol. 17, no. 5, 2025.
[19]
X. Li et al.,
"Chatter-free milling of aerospace thin-walled parts,"
Journal of Materials Processing Technology, vol. 341, 2025.
[20]
J. Cai et al.,
"Decision-Making in Complementary Products Supply Chain : Game Theory and Sensitivity Analysis,"
SYSTEMS, vol. 13, no. 5, 2025.
[21]
S. Liu et al.,
"A Digital Twin-Enabled Approach to Reliable Human–robot Collaborative Assembly,"
i Human Centric Smart Manufacturing Towards Industry 5 0, : Springer Nature, 2025, s. 281-304.
[22]
B. Wang et al.,
"Future Research Directions on Human-Centric Smart Manufacturing,"
i Human Centric Smart Manufacturing Towards Industry 5 0, : Springer Nature, 2025, s. 359-369.
[23]
"Human-Centric Smart Manufacturing Towards Industry 5.0,"
, Springer Nature, Human Centric Smart Manufacturing Towards Industry 5 0, 2025.
[24]
B. Wang et al.,
"Preface,"
i Human Centric Smart Manufacturing Towards Industry 5 0, : Springer Nature, 2025.
[25]
S. Zhao et al.,
"Industrial Foundation Models (IFMs) for intelligent manufacturing : A systematic review,"
Journal of manufacturing systems, vol. 82, s. 420-448, 2025.
[26]
Z. Liu et al.,
"Establishment and Synchronisation of Digital Twins for Multi-robot Systems in Manufacturing,"
i 58th CIRP Conference on Manufacturing Systems, CMS 2025, 2025, s. 419-424.
[27]
D. Antonelli et al.,
"Enhancing Industrial Mobile Manipulators Through Cognitive Digital Twins,"
i Innovations in Industrial Engineering IV, 2025, s. 25-36.
[28]
C. Zhang et al.,
"A digital twin shop-floor construction method towards seamless and resilient control,"
Journal of manufacturing systems, vol. 82, s. 660-677, 2025.
[29]
J. Leng et al.,
"Diffusion model-driven smart design and manufacturing : Prospects and challenges,"
Journal of manufacturing systems, vol. 82, s. 561-577, 2025.
[30]
Z. Chen et al.,
"Collaborative task scheduling in IIoT: A comparative study of evolutionary algorithms and deep reinforcement learning,"
Journal of Industrial Information Integration, vol. 47, 2025.
[31]
L. Ren et al.,
"Foundation Models for the Process Industry: Challenges and Opportunities,"
Engineering, 2025.
[32]
X. Li et al.,
"MSDF-VAE : A Cloud-Edge Collaborative Method for Fault Diagnosis Based on Transfer Learning,"
IEEE Internet of Things Journal, vol. 12, no. 12, s. 22393-22403, 2025.
[33]
L. Xu et al.,
"Energy flexible manufacturing systems : A systematic literature review, trends and challenges,"
Journal of manufacturing systems, vol. 82, s. 83-97, 2025.
[34]
M. Ding et al.,
"Cutting force modeling of the Gradient Helix Edge end mill based on Helix Angle Differentiation,"
Thin-walled structures, vol. 215, 2025.
[35]
T. Li et al.,
"Fusing model-based and data-driven prognostic methods for real-time model updating,"
Mechanical systems and signal processing, vol. 238, 2025.
[36]
J. Leng et al.,
"Physics-informed machine learning in intelligent manufacturing : a review,"
Journal of Intelligent Manufacturing, 2025.
[37]
N. Rea Minango, A. Maffei och M. Hedlind,
"Integrating assembly considerations into CAD: preliminary insights from industry practitioners,"
i 35th CIRP Design, CIRP Design 2025, 2025, s. 224-229.
[38]
L. Yongkui et al.,
"Digital Twinned Industrial Robot: Conceptual Framework, Key Technologies, and Case Study,"
Xitong Fangzhen Xuebao Journal of System Simulation, vol. 37, no. 7, s. 1723-1752, 2025.
[39]
D. Lombardi, L. Traetta och A. Maffei,
"ChatGPT and Instructional Design : An Ally for Inclusion?,"
i Inclusion, Communication, and Social Engagement - 1st International Conference, ICS exchange 2024, Proceedings, 2025, s. 124-135.
[40]
G. Wang et al.,
"Multi-robot collaborative manufacturing driven by digital twins : Advancements, challenges, and future directions,"
Journal of manufacturing systems, vol. 82, s. 333-361, 2025.
[41]
F. Mo et al.,
"Development of a runtime-condition model for proactive intelligent products using knowledge graphs and embedding,"
Knowledge-Based Systems, vol. 318, 2025.
[42]
B. Yao et al.,
"Research on Human-robot Collaborative Assembly Sequence Replanning Based on the Human Muscle Activation Fatigue Model,"
Jixie Gongcheng Xuebao Journal of Mechanical Engineering, vol. 61, no. 15, s. 339-350, 2025.
[43]
S. N. Rea Minango,
"Assembly features in collaborative product development : Integrating assembly into product information to enhance stakeholder communication,"
Doktorsavhandling Stockholm : KTH Royal Institute of Technology, TRITA-ITM-AVL, 2025:1, 2025.
[44]
B. Qiu et al.,
"Dynamic adaptive fault diagnosis using multi-channel image fusion and deep learning in channel failure occasions on rolling bearings,"
Advanced Engineering Informatics, vol. 68, 2025.
[45]
A. Pacini et al.,
"Digital thread in fixture design: leveraging model-based definition for seamless information flow,"
International journal of computer integrated manufacturing (Print), 2025.
[46]
J. Yuan et al.,
"Data augmentation-aided fatigue delamination shape prognostics in composites,"
Composite structures, vol. 374, 2025.
[47]
J. Yan et al.,
"Human-centric artificial intelligence towards Industry 5.0 : retrospect and prospect,"
Journal of Industrial Information Integration, vol. 47, 2025.
[48]
J. Leng et al.,
"High-performance manufacturing systems : concepts, performance metrics, enablers, challenges, and research directions,"
Advanced Engineering Informatics, vol. 68, 2025.
[49]
X. Deng et al.,
"Research on Decision of Echelon Utilization of Retired Power Batteries Under Government Regulation,"
World Electric Vehicle Journal, vol. 16, no. 7, 2025.
[50]
S. Liu och L. Wang,
"Vision intelligence-conditioned reinforcement learning for precision assembly,"
CIRP annals, vol. 74, no. 1, s. 13-17, 2025.