Publikationer
Publikationer inom Hållbara produktionssystem
[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, s. 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, s. 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, s. 8579-8588, 2022.
[6]
T. Fuoco et al.,
"Hydrogel Polyester Scaffolds via Direct-Ink-Writing of Ad Hoc Designed Photocurable Macromonomer,"
Polymers, vol. 14, no. 4, 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, s. 1-20, 2022.
[8]
X. Li et al.,
"Systematic review on tool breakage monitoring techniques in machining operations,"
International journal of machine tools & manufacture, vol. 176, 2022.
[9]
Y. Shi et al.,
"A Cognitive Digital Twins Framework for Human-Robot Collaboration,"
i 3Rd International Conference On Industry 4.0 And Smart Manufacturing, 2022, s. 1867-1874.
[10]
S. Liu, L. Wang och X. V. Wang,
"Multimodal Data-Driven Robot Control for Human-Robot Collaborative Assembly,"
Journal of manufacturing science and engineering, vol. 144, no. 5, 2022.
[11]
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, s. 3963-3982, 2022.
[12]
A. de Giorgio et al.,
"Assessing the influence of expert video aid on assembly learning curves,"
Journal of manufacturing systems, vol. 62, s. 263-269, 2022.
[13]
J. Jiang et al.,
"The state of the art of search strategies in robotic assembly,"
Journal of Industrial Information Integration, vol. 26, s. 100259, 2022.
[14]
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.
[15]
Y. Lu et al.,
"Semantic artificial intelligence for smart manufacturing automation,"
Robotics and Computer-Integrated Manufacturing, vol. 77, 2022.
[16]
S. Huang et al.,
"Industry 5.0 and Society 5.0-Comparison, complementation and co-evolution,"
Journal of manufacturing systems, vol. 64, s. 424-428, 2022.
[17]
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, s. 8383-8390, 2022.
[18]
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.
[19]
Q. Ji et al.,
"Online reinforcement learning for the shape morphing adaptive control of 4D printed shape memory polymer,"
Control Engineering Practice, vol. 126, s. 105257-105257, 2022.
[20]
Q. Ji et al.,
"Customized protective visors enabled by closed loop controlled 4D printing,"
Scientific Reports, vol. 12, no. 1, 2022.
[21]
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, s. 7145-7157, 2022.
[22]
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, s. 9057-9067, 2022.
[23]
Q. Ji et al.,
"Development of a 3D Printed Multi-Axial Force Sensor,"
i Advances in Transdisciplinary Engineering, : IOS Press, 2022.
[24]
Y. Jeong et al.,
"Digital Twin-Based Services and Data Visualization of Material Handling Equipment in Smart Production Logistics Environment,"
i Advances in Production Management Systems. Smart Manufacturing and Logistics Systems : Turning Ideas into Action, 2022, s. 556-564.
[25]
Q. Ji,
"Learning-based Control for 4D Printing and Soft Robotics,"
Doktorsavhandling Stockholm : Kungliga tekniska högskolan, TRITA-ITM-AVL, 2022:32, 2022.
[26]
M. H. Islam,
"Operational performance driven production system design process,"
Licentiatavhandling Sweden : KTH Royal Institute of Technology, TRITA-ITM-AVL, 35, 2022.
[27]
P. Zheng et al.,
"A visual reasoning-based approach for mutual-cognitive human-robot collaboration,"
CIRP annals, vol. 71, no. 1, s. 377-380, 2022.
[28]
D. Zhang et al.,
"Broadband Over-the-Air Computation for Federated Learning in Industrial IoT,"
i IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society, 2022.
[29]
S. Liu,
"Multimodal Human-Robot Collaboration in Assembly,"
Doktorsavhandling Brinellvägen 68, 114 28 Stockholm, Sweden : KTH Royal Institute of Technology, TRITA-ITM-AVL, 2022:12, 2022.
[30]
E. Flores-García et al.,
"Enabling industrial internet of things-based digital servitization in smart production logistics,"
International Journal of Production Research, s. 1-26, 2022.
[31]
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, s. 102382-102382, 2022.
[32]
[33]
E. Flores-García et al.,
"Digital Twin-Based Services for Smart Production Logistics,"
i 2021 Winter Simulation Conference (WSC); WSC 2021 Phoenix; 12 December 2021 - 15 December 2021., 2021.
[34]
C. Yang et al.,
"Flexible Resource Scheduling for Software-Defined Cloud Manufacturing with Edge Computing,"
Engineering, 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, s. 147-166, 2021.
[36]
P. Troll, K. Szipka och A. Archenti,
"Performance evaluation of LiDAR-based position measurement system,"
i Proceedings of the 21st International Conference of the European Society for Precision Engineering and Nanotechnology, EUSPEN 2021, 2021, s. 325-326.
[37]
A. de Giorgio et al.,
"Assessing the influence of expert video aid on assembly learning curves,"
, 2021.
[38]
A. de Giorgio et al.,
"Measuring the effect of automatically authored video aid on assembly time for procedural knowledge transfer among operators in adaptive assembly stations,"
International Journal of Production Research, 2021.
[39]
S. Gu et al.,
"Gaze Estimation via a Differential Eyes' Appearances Network with a Reference Grid,"
ENGINEERING, vol. 7, no. 6, s. 777-786, 2021.
[40]
Y. Lu et al.,
"y Humans Are Not Machines-Anthropocentric Human-Machine Symbiosis for Ultra-Flexible Smart Manufacturing,"
ENGINEERING, vol. 7, no. 6, s. 734-737, 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, s. 299-309, 2021.
[42]
Y. Liu et al.,
"Industrial Internet for Manufacturing,"
Robotics and Computer-Integrated Manufacturing, vol. 70, 2021.
[43]
B. Meng et al.,
"Uniformity, Periodicity and Symmetry Characteristics of Forces Fluctuation in Helical-Edge Milling Cutter,"
Applied Sciences, vol. 11, no. 6, 2021.
[44]
A. de Giorgio et al.,
"Towards online reinforced learning of assembly sequence planning with interactive guidance systems for industry 4.0 adaptive manufacturing,"
Journal of manufacturing systems, vol. 60, s. 22-34, 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, s. 491-506, 2021.
[46]
F. Tao et al.,
"Digital twin towards smart manufacturing and industry 4.0,"
Journal of manufacturing systems, vol. 58, s. 1-2, 2021.
[47]
M. M. Mabkhot et al.,
"Mapping Industry 4.0 Enabling Technologies into United Nations Sustainability Development Goals,"
Sustainability, vol. 13, no. 5, 2021.
[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.