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Publications in Industrial Production Systems

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

[1]
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.
[2]
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.
[3]
Z. Zhao et al., "Spatial-temporal traceability for cyber-physical industry 4.0 systems," Journal of manufacturing systems, vol. 74, pp. 16-29, 2024.
[4]
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.
[5]
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.
[6]
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.
[7]
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.
[8]
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.
[9]
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.
[10]
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.
[11]
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.
[12]
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.
[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]
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.
[18]
C. Yang et al., "Flexible Resource Scheduling for Software-Defined Cloud Manufacturing with Edge Computing," Engineering, vol. 22, pp. 60-70, 2023.
[19]
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.
[20]
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.
[21]
N. Rea Minango and A. Maffei, "Functional information integration in product development by using assembly features," in Procedia CIRP, 2023, pp. 254-259.
[22]
F. Lupi et al., "Automatic definition of engineer archetypes : A text mining approach," Computers in industry (Print), vol. 152, 2023.
[23]
P. Jiang et al., "Energy consumption prediction and optimization of industrial robots based on LSTM," Journal of manufacturing systems, vol. 70, pp. 137-148, 2023.
[24]
K. Ericsson and A. Maffei, "A Systematic Literature Review on Combinations of Industry 4.0 and Lean Production," in Advances in Production Management Systems. Production Management Systems for Responsible Manufacturing, Service, and Logistics Futures : IFIP WG 5.7 International Conference, APMS 2023, Proceedings, 2023, pp. 139-156.
[25]
W. Wu et al., "Deep discriminative clustering and structural constraint for cross-domain fault diagnosis of rotating machinery," Manufacturing Letters, vol. 35, pp. 1072-1080, 2023.
[26]
N. Rea Minango and A. Maffei, "Using physical interfaces for product design: from design to assembly planning," in Procedia CIRP, 56th CIRP International Conference on Manufacturing Systems, CIRP CMS 2023, 2023, pp. 1303-1308.
[27]
F. M. Monetti and A. Maffei, "Feeding-as-a-Service in a cloud manufacturing environment," in 56th CIRP International Conference on Manufacturing Systems, CIRP CMS 2023, 2023, pp. 1387-1392.
[28]
E. Boffa and A. Maffei, "Development and application of an Integrated Business Model framework to describe the digital transformation of manufacturing - a bibliometric analysis," Production & Manufacturing Research, vol. 11, no. 1, 2023.
[29]
B. Zhang et al., "An imbalanced data learning approach for tool wear monitoring based on data augmentation," Journal of Intelligent Manufacturing, 2023.
[30]
N. Rea Minango et al., "Identification and Categorization of Assembly Information for Collaborative Product Realization," in Towards Sustainable Customization: Bridging Smart Products and Manufacturing Systems : Proceedings of the Changeable, Agile, Reconfigurable and Virtual Production Conference and the World Mass Customization & Personalization ConferenceWorld Mass Customization & Personalization Conference, 2022, pp. 575-583.
[31]
X. Wei et al., "Tool wear state recognition based on feature selection method with whitening variational mode decomposition," Robotics and Computer-Integrated Manufacturing, vol. 77, 2022.
[32]
C. Yang et al., "Cloud-edge-device collaboration mechanisms of deep learning models for smart robots in mass personalization," Robotics and Computer-Integrated Manufacturing, vol. 77, pp. 102351, 2022.
[33]
C. Yue et al., "Research progress on machining deformation of thin-walled parts in milling process," Hangkong Xuebao/Acta Aeronautica et Astronautica Sinica, vol. 43, no. 4, 2022.
[34]
Y. Liu et al., "Logistics-involved service composition in a dynamic cloud manufacturing environment : A DDPG-based approach," Robotics and Computer-Integrated Manufacturing, vol. 76, pp. 102323, 2022.
[35]
X. Li et al., "Systematic review on tool breakage monitoring techniques in machining operations," International journal of machine tools & manufacture, vol. 176, 2022.
[36]
Y. Shi et al., "A Cognitive Digital Twins Framework for Human-Robot Collaboration," in 3Rd International Conference On Industry 4.0 And Smart Manufacturing, 2022, pp. 1867-1874.
[37]
A. Zhang et al., "Velocity effect sensitivity analysis of ball-end milling Ti-6Al-4 V," The International Journal of Advanced Manufacturing Technology, vol. 118, no. 11-12, pp. 3963-3982, 2022.
[38]
A. de Giorgio et al., "Assessing the influence of expert video aid on assembly learning curves," Journal of manufacturing systems, vol. 62, pp. 263-269, 2022.
[39]
J. Jiang et al., "The state of the art of search strategies in robotic assembly," Journal of Industrial Information Integration, vol. 26, pp. 100259, 2022.
[40]
J. Hua et al., "A zero-shot prediction method based on causal inference under non-stationary manufacturing environments for complex manufacturing systems," Robotics and Computer-Integrated Manufacturing, vol. 77, 2022.
[41]
Y. Lu et al., "Semantic artificial intelligence for smart manufacturing automation," Robotics and Computer-Integrated Manufacturing, vol. 77, 2022.
[42]
S. Huang et al., "Industry 5.0 and Society 5.0-Comparison, complementation and co-evolution," Journal of manufacturing systems, vol. 64, pp. 424-428, 2022.
[43]
Q. Ji et al., "Optimal shape morphing control of 4D printed shape memory polymer based on reinforcement learning," Robotics and Computer-Integrated Manufacturing, vol. 73, 2022.
[44]
Q. Ji et al., "Online reinforcement learning for the shape morphing adaptive control of 4D printed shape memory polymer," Control Engineering Practice, vol. 126, pp. 105257-105257, 2022.
[45]
Q. Ji et al., "Customized protective visors enabled by closed loop controlled 4D printing," Scientific Reports, vol. 12, no. 1, 2022.
[46]
X. Liu et al., "Surface roughness prediction method of titanium alloy milling based on CDH platform," The International Journal of Advanced Manufacturing Technology, vol. 119, no. 11-12, pp. 7145-7157, 2022.
[47]
L. Ren et al., "LM-CNN : A Cloud-Edge Collaborative Method for Adaptive Fault Diagnosis With Label Sampling Space Enlarging," IEEE Transactions on Industrial Informatics, vol. 18, no. 12, pp. 9057-9067, 2022.
[48]
Q. Ji et al., "Development of a 3D Printed Multi-Axial Force Sensor," in Advances in Transdisciplinary Engineering, : IOS Press, 2022.
[49]
Y. Jeong et al., "Digital Twin-Based Services and Data Visualization of Material Handling Equipment in Smart Production Logistics Environment," in Advances in Production Management Systems. Smart Manufacturing and Logistics Systems : Turning Ideas into Action, 2022, pp. 556-564.
[50]
N. A. Theissen et al., "Towards quasi-static kinematic calibration of serial articulated industrial manipulators," in MED 2022 30th Mediterranean Conference on Control and Automation, 2022, pp. 872-877.
Full list in the KTH publications portal