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Publications in Industrial Production Systems

Here are the 50 latest publications from the Unit of Industrial Production Systems.

[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, pp. 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, pp. 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, pp. 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," Doctoral thesis 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, pp. 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, pp. 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," in Structural Health Monitoring - The 10th Asia-Pacific Workshop on Structural Health Monitoring, 10APWSHM 2024, 2025, pp. 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," in Lecture Notes in Mechanical Engineering, : Springer Nature, 2024, pp. 3-38.
[16]
D. Mourtzis et al., "Modelling, Design and Simulation as-a-Service Based on Extended Reality (XR) in Industry 4.0," in CIRP Novel Topics in Production Engineering: Volume 1, : Springer Nature, 2024, pp. 99-143.
[17]
Z. Zhao et al., "Spatial-temporal traceability for cyber-physical industry 4.0 systems," Journal of manufacturing systems, vol. 74, pp. 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, pp. 192-204, 2024.
[19]
F. M. Monetti and A. Maffei, "Towards the definition of assembly-oriented modular product architectures: a systematic review," Research in Engineering Design, vol. 35, no. 2, pp. 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, pp. 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, pp. 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, pp. 349-363, 2024.
[23]
D. Antonelli et al., "Exploring the limitations and potential of digital twins for mobile manipulators in industry," in 5th International Conference on Industry 4.0 and Smart Manufacturing (ISM 2023), 2024, pp. 1121-1130.
[24]
F. M. Monetti, P. Z. Martínez and A. Maffei, "Assessing sustainable recyclability of battery systems: a tool to aid design for disassembly," in Proceedings of the Design Society, Design 2024, 2024, pp. 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, pp. 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, pp. 4069-4082, 2024.
[27]
S. Li, P. Zheng and L. Wang, "Self-organizing multi-agent teamwork," in Proactive Human-Robot Collaboration Toward Human-Centric Smart Manufacturing, : Elsevier BV, 2024, pp. 121-148.
[28]
S. Li, P. Zheng and L. Wang, "Preface," in Proactive Human-Robot Collaboration Toward Human-Centric Smart Manufacturing, : Elsevier BV, 2024.
[29]
S. Li, P. Zheng and L. Wang, "Deployment roadmap of proactive human–robot collaboration," in Proactive Human-Robot Collaboration Toward Human-Centric Smart Manufacturing, : Elsevier BV, 2024, pp. 149-192.
[30]
S. Li, P. Zheng and L. Wang, "Conclusions and future perspectives," in Proactive Human-Robot Collaboration Toward Human-Centric Smart Manufacturing, : Elsevier BV, 2024, pp. 265-279.
[31]
S. Li, P. Zheng and L. Wang, "Case studies of proactive human–robot collaboration in manufacturing," in Proactive Human-Robot Collaboration Toward Human-Centric Smart Manufacturing, : Elsevier BV, 2024, pp. 229-264.
[32]
S. Li, P. Zheng and L. Wang, "Evolution of human–robot relationships," in Proactive Human-Robot Collaboration Toward Human-Centric Smart Manufacturing, : Elsevier BV, 2024, pp. 9-26.
[33]
S. Li, P. Zheng and L. Wang, "Fundamentals of proactive human–robot collaboration," in Proactive Human-Robot Collaboration Toward Human-Centric Smart Manufacturing, : Elsevier BV, 2024, pp. 27-57.
[34]
S. Li, P. Zheng and L. Wang, "Introduction," in Proactive Human-Robot Collaboration Toward Human-Centric Smart Manufacturing, : Elsevier BV, 2024, pp. 1-8.
[35]
J. Guo et al., "Industrial metaverse towards Industry 5.0 : Connotation, architecture, enablers, and challenges," Journal of manufacturing systems, vol. 76, pp. 25-42, 2024.
[36]
S. Li, P. Zheng and 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, pp. 13-16, 2024.
[38]
F. M. Monetti, M. Bertoni and A. Maffei, "A Systematic Literature Review:Key Performance Indicatorson Feeding-as-a-Service," in Sustainable Production through Advanced Manufacturing, Intelligent Automation and Work Integrated Learning : Proceedings of the 11th Swedish Production Symposium (SPS2024), 2024, pp. 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, pp. 158-187, 2024.
[40]
S. Li et al., "Industrial Metaverse : A proactive human-robot collaboration perspective," Journal of manufacturing systems, vol. 76, pp. 314-319, 2024.
[41]
D. Mourtzis and L. Wang, "Industry 5.0: perspectives, concepts, and technologies," in Manufacturing from Industry 4.0 to Industry 5.0: Advances and Applications, : Elsevier, 2024, pp. 63-96.
[42]
X. V. Wang et al., "A literature survey of smart manufacturing systems for medical applications," Journal of manufacturing systems, vol. 76, pp. 502-519, 2024.
[43]
F. Lupi et al., "Ontology for Constructively Aligned, Collaborative, and Evolving Engineer Knowledge-Management Platforms," in Higher Education Learning Methodologies and Technologies Online - 5th International Conference, HELMeTO 2023, Revised Selected Papers, 2024, pp. 142-154.
[44]
E. Boffa and 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," Doctoral thesis 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, pp. 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, pp. 834-847, 2024.
[50]
Y. Wang et al., "Towards Industrial Foundation Models : Framework, Key Issues and Potential Applications," in Proceedings of the 2024 27th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2024, 2024, pp. 3269-3274.
Full list in the KTH publications portal