Publikationer
Här visas de 50 senaste publikationerna från institutionen för Produktionsutveckling.
[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]
S. Das et al.,
"Towards gamification for spatial digital learning environments,"
Entertainment Computing, vol. 52, 2025.
[4]
T. Wang et al.,
"A human-inspired slow-fast dual-branch method for product quality prediction of complex manufacturing processes with hierarchical variations,"
Advanced Engineering Informatics, vol. 64, 2025.
[5]
[6]
X. Wang et al.,
"Dynamic multi-tour order picking in an automotive-part warehouse based on attention-aware deep reinforcement learning,"
Robotics and Computer-Integrated Manufacturing, vol. 94, s. 102959-102959, 2025.
[7]
Z. Wang et al.,
"A novel hybrid LSTM and masked multi-head attention based network for energy consumption prediction of industrial robots,"
Applied Energy, vol. 383, 2025.
[8]
[9]
Q. Liu et al.,
"A method for remaining useful life prediction of milling cutter using multi-scale spatial data feature visualization and domain separation prediction network,"
Mechanical systems and signal processing, vol. 225, 2025.
[10]
B. Wang et al.,
"A deep learning-enabled visual-inertial fusion method for human pose estimation in occluded human-robot collaborative assembly scenarios,"
Robotics and Computer-Integrated Manufacturing, vol. 93, 2025.
[11]
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.
[12]
Q. Wang et al.,
"A phased robotic assembly policy based on a PL-LSTM-SAC algorithm,"
Journal of manufacturing systems, vol. 78, s. 351-369, 2025.
[13]
Z. Wang et al.,
"Energy consumption modeling based on operation mechanisms of industrial robots,"
Robotics and Computer-Integrated Manufacturing, vol. 94, 2025.
[14]
S. R. Kalvakolu et al.,
"Combining 360° Spaces and Social VR,"
i Games and Learning Alliance - 13th International Conference, GALA 2024, Proceedings, 2025, s. 375-380.
[15]
B. Zhang et al.,
"An imbalanced data learning approach for tool wear monitoring based on data augmentation,"
Journal of Intelligent Manufacturing, vol. 36, no. 1, s. 399-420, 2025.
[16]
J. Leng et al.,
"Federated learning-empowered smart manufacturing and product lifecycle management : A review,"
Advanced Engineering Informatics, vol. 65, 2025.
[17]
M. Gonzalez, M. J. Coll-Araoz och A. Archenti,
"Enhancing reliability in advanced manufacturing systems : A methodology for the assessment of detection and monitoring techniques,"
Journal of manufacturing systems, vol. 79, s. 318-333, 2025.
[18]
B. Wang et al.,
"Context-aware AR adaptive information push for product assembly: Aligning information load with human cognitive abilities,"
Advanced Engineering Informatics, vol. 64, 2025.
[19]
S. N. Rea Minango,
"Assembly features in collaborative product development : Integrating assembly into product information to enhance stakeholder communication,"
Doktorsavhandling Stockholm : KTH Royal Institute of Technology, TRITA-ITM-AVL, 2025:1, 2025.
[20]
J. Leng et al.,
"Review of manufacturing system design in the interplay of Industry 4.0 and Industry 5.0 (Part II) : Design processes and enablers,"
Journal of manufacturing systems, vol. 79, s. 528-562, 2025.
[21]
Z. Wang et al.,
"Industrial Robots Energy Consumption Modeling, Identification and Optimization Through Time-Scaling,"
IEEE Transactions on robotics, vol. 41, s. 1456-1475, 2025.
[22]
B. Ganesh et al.,
"Towards a Circular Solution for Healthcare Plastic Waste : Understanding the Legal, Operational, and Technological Landscape,"
Recycling, vol. 10, no. 1, 2025.
[23]
[24]
C. Zhang et al.,
"Transfer learning and augmented data-driven parameter prediction for robotic welding,"
Robotics and Computer-Integrated Manufacturing, vol. 95, 2025.
[25]
[26]
[27]
[28]
X. Deng, Z. Wang och Y. Wang,
"Practical Research on Intelligent Upgrading Management of Building Steel Structure Manufacturing Factory,"
i Proceedings of the 14th International Conference on Logistics and Systems Engineering, 2025, s. 268-278.
[29]
T. Wang et al.,
"A design framework for high-fidelity human-centric digital twin of collaborative work cell in Industry 5.0,"
Journal of manufacturing systems, vol. 80, s. 140-156, 2025.
[30]
[31]
[32]
E. Flores-García et al.,
"Machine learning in smart production logistics : a review of technological capabilities,"
International Journal of Production Research, vol. 63, no. 5, s. 1898-1932, 2025.
[33]
H. U. Rehman et al.,
"Intelligent configuration management in modular production systems : Integrating operational semantics with knowledge graphs,"
Journal of manufacturing systems, vol. 80, s. 610-625, 2025.
[34]
M. Zafarzadeh et al.,
"A framework and system architecture for value-oriented digital services in data-driven production logistics,"
International Journal of Production Research, s. 1-21, 2025.
[35]
P. Dunaj et al.,
"Stiffness-controlled lathe spindle for varying operating conditions,"
The International Journal of Advanced Manufacturing Technology, vol. 137, no. 9-10, s. 4521-4535, 2025.
[36]
J. Byström och M. M. Sharifi,
"Optimering av inbound-processen hos DeLavals fabrik i Tumba,"
, 2025.
[37]
M. K. Gonzalez Bassante,
"On the Accuracy of Articulated Robots : A Comprehensive Approach to Evaluate and Improve Robot Accuracy for Contact Applications,"
Doktorsavhandling Stockholm : KTH Royal Institute of Technology, TRITA-ITM-AVL, 2025:6, 2025.
[38]
[39]
[40]
R. Kalaiarasan,
"Visibility in Manufacturing Supply Chains: Conceptualisation, Realisation and Implications,"
Doktorsavhandling Stockholm : KTH Royal Institute of Technology, TRITA-ITM-AVL, 2025:12, 2025.
[41]
K. Käll och K. Rahmani,
"LCA-aspekter vid verktygsrekommendationer : Utveckling av stöd för val av verktyg och skärdata,"
, 2025.
[42]
W. Farzad och N. Malakar,
"Predicting Quality of Surface Roughness and Tool Wear by Different Signals and Regression Algorithms,"
, 2025.
[43]
[44]
M. P. Tay et al.,
"A simulation-based decision support tool for circular manufacturing systems in the automotive industry using electric machines as a remanufacturing case study,"
International Journal of Production Research, s. 1-20, 2025.
[45]
J. Chen et al.,
"Fabrication and development of mechanical metamaterials via additive manufacturing for biomedical applications : a review,"
International Journal of Extreme Manufacturing, vol. 7, no. 1, 2025.
[46]
K. Yang et al.,
"A multi-level multi-domain digital twin modeling method for industrial robots,"
Robotics and Computer-Integrated Manufacturing, vol. 95, 2025.
[47]
A. de Giorgio,
"From entropy to international relations: How research into artificial intelligence is improving the world,"
i The Routledge Handbook of Artificial Intelligence and International Relations, : Informa UK Limited, 2025, s. 5-18.
[48]
D. Brasioli et al.,
"Introduction: The transformative impact of artificial intelligence on our world,"
i The Routledge Handbook of Artificial Intelligence and International Relations, : Informa UK Limited, 2025, s. 1-2.
[49]
Z. Zhao et al.,
"Enhancing reconfiguration of cloud manufacturing service composition under unexpected changes in service time availability by flexible splitting and intermingling strategies,"
Robotics and Computer-Integrated Manufacturing, vol. 95, 2025.
[50]
X. Li et al.,
"Chatter-free milling of aerospace thin-walled parts,"
Journal of Materials Processing Technology, vol. 341, 2025.