Publikationer
Här visas de 50 senaste publikationerna från institutionen för Produktionsutveckling.
[1]
Z. Zhou et al.,
"Learning accurate and efficient three-finger grasp generation in clutters with an auto-annotated large-scale dataset,"
Robotics and Computer-Integrated Manufacturing, vol. 91, 2025.
[2]
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.
[3]
Y. Qin et al.,
"A tool wear monitoring method based on data-driven and physical output,"
Robotics and Computer-Integrated Manufacturing, vol. 91, 2025.
[4]
S. Das et al.,
"Towards gamification for spatial digital learning environments,"
Entertainment Computing, vol. 52, 2025.
[5]
S. Liu, L. Wang och R. X. Gao,
"Cognitive neuroscience and robotics : Advancements and future research directions,"
Robotics and Computer-Integrated Manufacturing, vol. 85, 2024.
[6]
X. Li et al.,
"ACWGAN-GP for milling tool breakage monitoring with imbalanced data,"
Robotics and Computer-Integrated Manufacturing, vol. 85, 2024.
[7]
X. Zhang et al.,
"Knowledge graph and function block based Digital Twin modeling for robotic machining of large-scale components,"
Robotics and Computer-Integrated Manufacturing, vol. 85, s. 102609, 2024.
[8]
B. Wang et al.,
"Human Digital Twin in the context of Industry 5.0,"
Robotics and Computer-Integrated Manufacturing, vol. 85, 2024.
[9]
M. Chodnicki et al.,
"Project-Based Collaborative Research and Training Roadmap for Manufacturing Based on Industry 4.0,"
i Flexible Automation and Intelligent Manufacturing : Establishing Bridges for More Sustainable Manufacturing Systems, 2024, s. 708-715.
[10]
Y. Zhang et al.,
"Skeleton-RGB integrated highly similar human action prediction in human–robot collaborative assembly,"
Robotics and Computer-Integrated Manufacturing, vol. 86, 2024.
[11]
X. Li et al.,
"Data-model linkage prediction of tool remaining useful life based on deep feature fusion and Wiener process,"
Journal of manufacturing systems, vol. 73, s. 19-38, 2024.
[12]
Z. Zhang et al.,
"A residual reinforcement learning method for robotic assembly using visual and force information,"
Journal of manufacturing systems, vol. 72, s. 245-262, 2024.
[13]
Z. Huang et al.,
"Cross-domain tool wear condition monitoring via residual attention hybrid adaptation network,"
Journal of manufacturing systems, vol. 72, s. 406-423, 2024.
[14]
B. Yao et al.,
"Virtual data generation for human intention prediction based on digital modeling of human-robot collaboration,"
Robotics and Computer-Integrated Manufacturing, vol. 87, 2024.
[15]
[16]
M. Urgo et al.,
"AI-Based Pose Estimation of Human Operators in Manufacturing Environments,"
i Lecture Notes in Mechanical Engineering, : Springer Nature, 2024, s. 3-38.
[17]
X. Li et al.,
"Smart Reconfigurable Manufacturing: Literature Analysis,"
i 11th CIRP Global Web Conference, CIRPe 2023, 2024, s. 43-48.
[18]
D. Mourtzis et al.,
"Modelling, Design and Simulation as-a-Service Based on Extended Reality (XR) in Industry 4.0,"
i CIRP Novel Topics in Production Engineering: Volume 1, : Springer Nature, 2024, s. 99-143.
[19]
Y. Lu et al.,
"Smart manufacturing enabled by intelligent technologies,"
International journal of computer integrated manufacturing (Print), vol. 37, no. 1-2, s. 1-3, 2024.
[20]
Z. Zhao et al.,
"Spatial-temporal traceability for cyber-physical industry 4.0 systems,"
Journal of manufacturing systems, vol. 74, s. 16-29, 2024.
[21]
J. Fan et al.,
"An Integrated Hand-Object Dense Pose Estimation Approach With Explicit Occlusion Awareness for Human-Robot Collaborative Disassembly,"
IEEE Transactions on Automation Science and Engineering, vol. 21, no. 1, s. 147-156, 2024.
[22]
[23]
S. Yi et al.,
"Safety-aware human-centric collaborative assembly,"
Advanced Engineering Informatics, vol. 60, 2024.
[24]
D. Li et al.,
"An online inference method for condition identification of workpieces with complex residual stress distributions,"
Journal of manufacturing systems, vol. 73, s. 192-204, 2024.
[25]
X. Li et al.,
"Joint scheduling optimisation method for the machining and heat-treatment of hydraulic cylinders based on improved multi-objective migrating birds optimisation,"
Journal of manufacturing systems, vol. 73, s. 170-191, 2024.
[26]
X. Li et al.,
"Knowledge graph based OPC UA information model automatic construction method for heterogeneous devices integration,"
Robotics and Computer-Integrated Manufacturing, vol. 88, 2024.
[27]
Y. Jeong, E. Flores-García och M. Wiktorsson,
"Integrating Smart Production Logisticswith Network Diagrams: A Frameworkfor Data Visualization,"
i Proceedings of the 11th Swedish Production Symposium, 2024, s. 601-612.
[28]
F. M. Monetti och A. Maffei,
"Towards the definition of assembly-oriented modular product architectures: a systematic review,"
Research in Engineering Design, vol. 35, no. 2, s. 137-169, 2024.
[29]
C. Li et al.,
"Unleashing mixed-reality capability in Deep Reinforcement Learning-based robot motion generation towards safe human–robot collaboration,"
Journal of manufacturing systems, vol. 74, s. 411-421, 2024.
[30]
A. Aristeidou,
"Development of a Business Model Framework for Collaborative Model-Based Engineering,"
, 2024.
[31]
[32]
F. Lupi, A. Maffei och M. Lanzetta,
"CAD-based Autonomous Vision Inspection Systems,"
i 5th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2023, 2024, s. 2127-2136.
[33]
S. Kokare et al.,
"Life Cycle Assessment of a Jet Printing and Dispensing Machine,"
i 5th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2023, 2024, s. 708-718.
[34]
M. Subasic et al.,
"Fatigue strength improvement of additively manufactured 316L stainless steel with high porosity through preloading,"
International Journal of Fatigue, vol. 180, 2024.
[35]
[36]
[37]
[38]
[39]
[40]
[41]
S. Amir et al.,
"Toward a Circular Economy: A Guiding Framework for Circular Supply Chain Implementation,"
i Springer Series in Supply Chain Management, : Springer Nature, 2024, s. 379-404.
[42]
S. Wei et al.,
"An improved memetic algorithm for multi-objective resource-constrained flexible job shop inverse scheduling problem : An application for machining workshop,"
Journal of manufacturing systems, vol. 74, s. 264-290, 2024.
[43]
Y. Wang et al.,
"Research on Pharmaceutical Supply Chain Decision-Making Model Considering Output and Demand Fluctuations,"
IEEE Access, vol. 12, s. 61629-61641, 2024.
[44]
B. Wang et al.,
"Towards the industry 5.0 frontier: Review and prospect of XR in product assembly,"
Journal of manufacturing systems, vol. 74, s. 777-811, 2024.
[45]
X. Li et al.,
"Flexible Job Shop Composite Dispatching Rule Mining Approach Based on an Improved Genetic Programming Algorithm,"
Tsinghua Science and Technology, vol. 29, no. 5, s. 1390-1408, 2024.
[46]
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, s. 349-363, 2024.
[47]
D. Antonelli et al.,
"Exploring the limitations and potential of digital twins for mobile manipulators in industry,"
i 5th International Conference on Industry 4.0 and Smart Manufacturing (ISM 2023), 2024, s. 1121-1130.
[48]
S. Linderson, S. E. Birkie och M. Bellgran,
"The Issue of Corporate Mandatory Standards in Production Improvement Programmes,"
Journal of Industrial Engineering and Management, vol. 17, no. 2, s. 385-402, 2024.
[49]
[50]