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Publikationer inom industriella produktionssystem

Här visas de 50 senaste publikationerna från enheten för industriella produktionssystem.

[1]
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.
[2]
Y. Qin et al., "A tool wear monitoring method based on data-driven and physical output," Robotics and Computer-Integrated Manufacturing, vol. 91, 2025.
[3]
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.
[4]
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.
[5]
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.
[6]
J. Leng et al., "Federated learning-empowered smart manufacturing and product lifecycle management : A review," Advanced Engineering Informatics, vol. 65, 2025.
[7]
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.
[8]
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.
[9]
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.
[10]
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.
[11]
X. Li et al., "Chatter-free milling of aerospace thin-walled parts," Journal of Materials Processing Technology, vol. 341, 2025.
[12]
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.
[13]
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.
[14]
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.
[15]
M. Urgo et al., "AI-Based Pose Estimation of Human Operators in Manufacturing Environments," i Lecture Notes in Mechanical Engineering, : Springer Nature, 2024, s. 3-38.
[16]
D. Mourtzis et al., "Modelling, Design and Simulation as-a-Service Based on Extended Reality (XR) in Industry 4.0," i CIRP Novel Topics in Production Engineering: Volume 1, : Springer Nature, 2024, s. 99-143.
[17]
Z. Zhao et al., "Spatial-temporal traceability for cyber-physical industry 4.0 systems," Journal of manufacturing systems, vol. 74, s. 16-29, 2024.
[18]
D. Li et al., "An online inference method for condition identification of workpieces with complex residual stress distributions," Journal of manufacturing systems, vol. 73, s. 192-204, 2024.
[19]
F. M. Monetti och A. Maffei, "Towards the definition of assembly-oriented modular product architectures: a systematic review," Research in Engineering Design, vol. 35, no. 2, s. 137-169, 2024.
[20]
Y. Wang et al., "Research on Pharmaceutical Supply Chain Decision-Making Model Considering Output and Demand Fluctuations," IEEE Access, vol. 12, s. 61629-61641, 2024.
[21]
B. Wang et al., "Towards the industry 5.0 frontier: Review and prospect of XR in product assembly," Journal of manufacturing systems, vol. 74, s. 777-811, 2024.
[22]
J. Leng et al., "Unlocking the power of industrial artificial intelligence towards Industry 5.0: Insights, pathways, and challenges," Journal of manufacturing systems, vol. 73, s. 349-363, 2024.
[23]
D. Antonelli et al., "Exploring the limitations and potential of digital twins for mobile manipulators in industry," i 5th International Conference on Industry 4.0 and Smart Manufacturing (ISM 2023), 2024, s. 1121-1130.
[24]
F. M. Monetti, P. Z. Martínez och A. Maffei, "Assessing sustainable recyclability of battery systems: a tool to aid design for disassembly," i Proceedings of the Design Society, Design 2024, 2024, s. 1389-1398.
[25]
K. Y. H. Lim et al., "Graph-enabled cognitive digital twins for causal inference in maintenance processes," International Journal of Production Research, vol. 62, no. 13, s. 4717-4734, 2024.
[26]
D. Zhang et al., "IRS Assisted Federated Learning : A Broadband Over-the-Air Aggregation Approach," IEEE Transactions on Wireless Communications, vol. 23, no. 5, s. 4069-4082, 2024.
[27]
S. Li, P. Zheng och L. Wang, "Self-organizing multi-agent teamwork," i Proactive Human-Robot Collaboration Toward Human-Centric Smart Manufacturing, : Elsevier BV, 2024, s. 121-148.
[28]
S. Li, P. Zheng och L. Wang, "Preface," i Proactive Human-Robot Collaboration Toward Human-Centric Smart Manufacturing, : Elsevier BV, 2024.
[29]
S. Li, P. Zheng och L. Wang, "Deployment roadmap of proactive human–robot collaboration," i Proactive Human-Robot Collaboration Toward Human-Centric Smart Manufacturing, : Elsevier BV, 2024, s. 149-192.
[30]
S. Li, P. Zheng och L. Wang, "Conclusions and future perspectives," i Proactive Human-Robot Collaboration Toward Human-Centric Smart Manufacturing, : Elsevier BV, 2024, s. 265-279.
[31]
S. Li, P. Zheng och L. Wang, "Case studies of proactive human–robot collaboration in manufacturing," i Proactive Human-Robot Collaboration Toward Human-Centric Smart Manufacturing, : Elsevier BV, 2024, s. 229-264.
[32]
S. Li, P. Zheng och L. Wang, "Evolution of human–robot relationships," i Proactive Human-Robot Collaboration Toward Human-Centric Smart Manufacturing, : Elsevier BV, 2024, s. 9-26.
[33]
S. Li, P. Zheng och L. Wang, "Fundamentals of proactive human–robot collaboration," i Proactive Human-Robot Collaboration Toward Human-Centric Smart Manufacturing, : Elsevier BV, 2024, s. 27-57.
[34]
S. Li, P. Zheng och L. Wang, "Introduction," i Proactive Human-Robot Collaboration Toward Human-Centric Smart Manufacturing, : Elsevier BV, 2024, s. 1-8.
[35]
J. Guo et al., "Industrial metaverse towards Industry 5.0 : Connotation, architecture, enablers, and challenges," Journal of manufacturing systems, vol. 76, s. 25-42, 2024.
[36]
S. Li, P. Zheng och L. Wang, Proactive Human–Robot Collaboration Toward Human-Centric Smart Manufacturing. Elsevier BV, 2024.
[37]
S. Liu et al., "Vision AI-based human-robot collaborative assembly driven by autonomous robots," CIRP annals, vol. 73, no. 1, s. 13-16, 2024.
[38]
F. M. Monetti, M. Bertoni och A. Maffei, "A Systematic Literature Review:Key Performance Indicatorson Feeding-as-a-Service," i Sustainable Production through Advanced Manufacturing, Intelligent Automation and Work Integrated Learning : Proceedings of the 11th Swedish Production Symposium (SPS2024), 2024, s. 256-267.
[39]
J. Leng et al., "Review of manufacturing system design in the interplay of Industry 4.0 and Industry 5.0 (Part I): Design thinking and modeling methods," Journal of manufacturing systems, vol. 76, s. 158-187, 2024.
[40]
S. Li et al., "Industrial Metaverse : A proactive human-robot collaboration perspective," Journal of manufacturing systems, vol. 76, s. 314-319, 2024.
[41]
D. Mourtzis och L. Wang, "Industry 5.0: perspectives, concepts, and technologies," i Manufacturing from Industry 4.0 to Industry 5.0: Advances and Applications, : Elsevier, 2024, s. 63-96.
[42]
X. V. Wang et al., "A literature survey of smart manufacturing systems for medical applications," Journal of manufacturing systems, vol. 76, s. 502-519, 2024.
[43]
F. Lupi et al., "Ontology for Constructively Aligned, Collaborative, and Evolving Engineer Knowledge-Management Platforms," i Higher Education Learning Methodologies and Technologies Online - 5th International Conference, HELMeTO 2023, Revised Selected Papers, 2024, s. 142-154.
[44]
E. Boffa och A. Maffei, "Investigating the impact of digital transformation on manufacturers’ Business model: Insights from Swedish industry," Journal of Open Innovation: Technology, Market, and Complexity, vol. 10, no. 2, 2024.
[45]
L. Kablitz, "Dependencies between innovativemanufacturing solutions andcommercialisation in the spaceindustry : A systematic literature review and multi-level perspectiveanalysis," , 2024.
[46]
E. Boffa, "Characterisation of the digital transformation in manufacturing : A holistic Business model framework," Doktorsavhandling Stockholm : KTH Royal Institute of Technology, TRITA-ITM-AVL, 2024:22, 2024.
[47]
J. Zhou et al., "BDTM-Net: A tool wear monitoring framework based on semantic segmentation module," Journal of manufacturing systems, vol. 77, s. 576-590, 2024.
[48]
Z. Lai et al., "BearingFM: Towards a foundation model for bearing fault diagnosis by domain knowledge and contrastive learning," International Journal of Production Economics, vol. 275, 2024.
[49]
Y. Lu et al., "Research on digital twin monitoring system during milling of large parts," Journal of manufacturing systems, vol. 77, s. 834-847, 2024.
[50]
Y. Wang et al., "Towards Industrial Foundation Models : Framework, Key Issues and Potential Applications," i Proceedings of the 2024 27th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2024, 2024, s. 3269-3274.
Fullständig lista i KTH:s publikationsportal