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Publications

Publications within Sustainable Production Systems

[1]
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.
[2]
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.
[3]
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.
[4]
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.
[5]
S. Li et al., "Toward Proactive Human-Robot Collaborative Assembly : A Multimodal Transfer-Learning-Enabled Action Prediction Approach," IEEE transactions on industrial electronics (1982. Print), vol. 69, no. 8, pp. 8579-8588, 2022.
[7]
T. K. Agrawal et al., "Demonstration of a blockchain-based framework using smart contracts for supply chain collaboration," International Journal of Production Research, pp. 1-20, 2022.
[8]
P. Wang et al., "The Existence of Autonomous Chaos in EDM Process," Machines, vol. 10, no. 4, 2022.
[9]
X. Li et al., "Systematic review on tool breakage monitoring techniques in machining operations," International journal of machine tools & manufacture, vol. 176, 2022.
[10]
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.
[11]
S. Liu, L. Wang and X. V. Wang, "Multimodal Data-Driven Robot Control for Human-Robot Collaborative Assembly," Journal of manufacturing science and engineering, vol. 144, no. 5, 2022.
[12]
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.
[13]
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.
[14]
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.
[16]
Y. Lu et al., "Semantic artificial intelligence for smart manufacturing automation," Robotics and Computer-Integrated Manufacturing, vol. 77, 2022.
[17]
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.
[18]
K. Tan et al., "Shape Estimation of a 3D Printed Soft Sensor Using Multi-Hypothesis Extended Kalman Filter," IEEE Robotics and Automation Letters, vol. 7, no. 3, pp. 8383-8390, 2022.
[19]
E. Flores-García et al., "Enabling industrial internet of things-based digital servitization in smart production logistics," International Journal of Production Research, pp. 1-26, 2022.
[20]
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.
[21]
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.
[22]
Q. Ji et al., "Customized protective visors enabled by closed loop controlled 4D printing," Scientific Reports, vol. 12, no. 1, 2022.
[23]
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.
[24]
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.
[25]
Q. Ji et al., "Synthesizing the optimal gait of a quadruped robot with soft actuators using deep reinforcement learning," Robotics and Computer-Integrated Manufacturing, vol. 78, pp. 102382-102382, 2022.
[26]
Q. Ji et al., "Development of a 3D Printed Multi-Axial Force Sensor," in Advances in Transdisciplinary Engineering, : IOS Press, 2022.
[27]
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.
[28]
Q. Ji, "Learning-based Control for 4D Printing and Soft Robotics," Doctoral thesis Stockholm : Kungliga tekniska högskolan, TRITA-ITM-AVL, 2022:32, 2022.
[29]
M. H. Islam, "Operational performance driven production system design process," Licentiate thesis Sweden : KTH Royal Institute of Technology, TRITA-ITM-AVL, 35, 2022.
[30]
P. Zheng et al., "A visual reasoning-based approach for mutual-cognitive human-robot collaboration," CIRP annals, vol. 71, no. 1, pp. 377-380, 2022.
[31]
D. Zhang et al., "Broadband Over-the-Air Computation for Federated Learning in Industrial IoT," in IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society, 2022.
[32]
S. Liu, "Multimodal Human-Robot Collaboration in Assembly," Doctoral thesis Brinellvägen 68, 114 28 Stockholm, Sweden : KTH Royal Institute of Technology, TRITA-ITM-AVL, 2022:12, 2022.
[33]
E. Flores-García et al., "Digital Twin-Based Services for Smart Production Logistics," in 2021 Winter Simulation Conference (WSC); WSC 2021 Phoenix; 12 December 2021 - 15 December 2021., 2021.
[35]
B. Meng et al., "Research Progress on the Architecture and Key Technologies of Machine Tool Intelligent Control System," Jixie Gongcheng Xuebao/Journal of Mechanical Engineering, vol. 57, no. 9, pp. 147-166, 2021.
[36]
P. Troll, K. Szipka and A. Archenti, "Performance evaluation of LiDAR-based position measurement system," in Proceedings of the 21st International Conference of the European Society for Precision Engineering and Nanotechnology, EUSPEN 2021, 2021, pp. 325-326.
[39]
S. Gu et al., "Gaze Estimation via a Differential Eyes' Appearances Network with a Reference Grid," ENGINEERING, vol. 7, no. 6, pp. 777-786, 2021.
[41]
X.-C. Xi et al., "Velocity planning in multi-axis EDM based on a coder-player architecture," Journal of manufacturing systems, vol. 59, pp. 299-309, 2021.
[42]
Y. Liu et al., "Industrial Internet for Manufacturing," Robotics and Computer-Integrated Manufacturing, vol. 70, 2021.
[45]
S. Treber et al., "Robust optimization of information flows in global production networks using multi-method simulation and surrogate modelling," CIRP - Journal of Manufacturing Science and Technology, vol. 32, pp. 491-506, 2021.
[46]
F. Tao et al., "Digital twin towards smart manufacturing and industry 4.0," Journal of manufacturing systems, vol. 58, pp. 1-2, 2021.
[47]
[48]
C. Yang et al., "Transforming Hong Kong's warehousing industry with a novel business model : A game-theory analysis," Robotics and Computer-Integrated Manufacturing, vol. 68, 2021.
[49]
K. Kang et al., "Auction-based cloud service allocation and sharing for logistics product service system," Journal of Cleaner Production, vol. 278, 2021.
[50]
H. Liang et al., "Logistics-involved QoS-aware service composition in cloud manufacturing with deep reinforcement learning," Robotics and Computer-Integrated Manufacturing, vol. 67, 2021.
Full list in the KTH publications portal