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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]
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.
[4]
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.
[5]
Z. Zhao et al., "Spatial-temporal traceability for cyber-physical industry 4.0 systems," Journal of manufacturing systems, vol. 74, pp. 16-29, 2024.
[6]
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.
[7]
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.
[8]
F. Lupi, A. Maffei and M. Lanzetta, "CAD-based Autonomous Vision Inspection Systems," in 5th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2023, 2024, pp. 2127-2136.
[9]
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.
[10]
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.
[11]
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.
[12]
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.
[13]
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.
[14]
B. Zhang et al., "Meta-learning-based approach for tool condition monitoring in multi-condition small sample scenarios," Mechanical systems and signal processing, vol. 216, 2024.
[15]
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.
[16]
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.
[17]
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.
[18]
S. Li, P. Zheng and L. Wang, "Preface," in Proactive Human-Robot Collaboration Toward Human-Centric Smart Manufacturing, : Elsevier BV, 2024.
[19]
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.
[20]
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.
[21]
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.
[22]
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.
[23]
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.
[24]
S. Li, P. Zheng and L. Wang, "Predictable spatio-temporal collaboration," in Proactive Human-Robot Collaboration Toward Human-Centric Smart Manufacturing, : Elsevier BV, 2024, pp. 93-120.
[25]
S. Li, P. Zheng and L. Wang, "Introduction," in Proactive Human-Robot Collaboration Toward Human-Centric Smart Manufacturing, : Elsevier BV, 2024, pp. 1-8.
[26]
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.
[27]
S. Li, P. Zheng and L. Wang, "Mutual-cognitive and empathic co-working," in Proactive Human-Robot Collaboration Toward Human-Centric Smart Manufacturing, : Elsevier BV, 2024, pp. 59-92.
[28]
S. Li, P. Zheng and L. Wang, Proactive Human–Robot Collaboration Toward Human-Centric Smart Manufacturing. Elsevier BV, 2024.
[29]
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.
[30]
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.
[31]
P. Zheng et al., "A vision-language-guided and deep reinforcement learning-enabled approach for unstructured human-robot collaborative manufacturing task fulfilment," CIRP annals, vol. 73, no. 1, pp. 341-344, 2024.
[32]
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.
[33]
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.
[34]
S. Li et al., "Industrial Metaverse : A proactive human-robot collaboration perspective," Journal of manufacturing systems, vol. 76, pp. 314-319, 2024.
[35]
J. Zhang et al., "Efficient data management for intelligent manufacturing," in Manufacturing from Industry 4.0 to Industry 5.0: Advances and Applications, : Elsevier BV, 2024, pp. 289-312.
[36]
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.
[37]
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.
[38]
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.
[39]
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.
[40]
L. Kablitz, "Dependencies between innovativemanufacturing solutions andcommercialisation in the spaceindustry : A systematic literature review and multi-level perspectiveanalysis," , 2024.
[41]
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.
[42]
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.
[43]
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.
[44]
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.
[45]
N. Rea Minango, M. Hedlind and A. Maffei, "Handling features in assembly: Integrating manufacturing considerations early in design discussions," Journal of manufacturing systems, vol. 77, pp. 1077-1100, 2024.
[46]
T. K. Agrawal et al., "Demonstration of a blockchain-based framework using smart contracts for supply chain collaboration," International Journal of Production Research, vol. 61, no. 5, pp. 1497-1516, 2023.
[47]
C. Yang et al., "Flexible Resource Scheduling for Software-Defined Cloud Manufacturing with Edge Computing," Engineering, vol. 22, pp. 60-70, 2023.
[48]
A. Maffei and F. Enoksson, "What is the optimal blended learning strategy throughout engineering curricula? Lesson learned during Covid-19 pandemic," in EDUCON 2023 - IEEE Global Engineering Education Conference, Proceedings, 2023.
[49]
X. Wei et al., "A multi-sensor signals denoising framework for tool state monitoring based on UKF-CycleGAN," Mechanical systems and signal processing, vol. 200, 2023.
[50]
N. Rea Minango and A. Maffei, "Functional information integration in product development by using assembly features," in Procedia CIRP, 2023, pp. 254-259.
Full list in the KTH publications portal