Publikationer inom industriella produktionssystem
Här visas de 50 senaste publikationerna från enheten för industriella produktionssystem.
[1]
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.
[2]
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.
[3]
Z. Zhao et al.,
"Spatial-temporal traceability for cyber-physical industry 4.0 systems,"
Journal of manufacturing systems, vol. 74, s. 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, s. 192-204, 2024.
[5]
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.
[6]
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.
[7]
F. M. Monetti, M. Bertoni och A. Maffei,
"A Systematic Literature Review:Key Performance Indicatorson Feeding-as-a-Service,"
i Sustainable Production through Advanced Manufacturing, Intelligent Automation and Work Integrated Learning : Proceedings of the 11th Swedish Production Symposium (SPS2024), 2024, s. 256-267.
[8]
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.
[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, s. 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, s. 349-363, 2024.
[11]
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.
[12]
E. Boffa och 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 och A. Maffei,
"Assessing sustainable recyclability of battery systems: a tool to aid design for disassembly,"
i Proceedings of the Design Society, Design 2024, 2024, s. 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, s. 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, s. 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, s. 1497-1516, 2023.
[18]
C. Yang et al.,
"Flexible Resource Scheduling for Software-Defined Cloud Manufacturing with Edge Computing,"
Engineering, vol. 22, s. 60-70, 2023.
[19]
A. Maffei och F. Enoksson,
"What is the optimal blended learning strategy throughout engineering curricula? Lesson learned during Covid-19 pandemic,"
i 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 och A. Maffei,
"Functional information integration in product development by using assembly features,"
i Procedia CIRP, 2023, s. 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, s. 137-148, 2023.
[24]
K. Ericsson och A. Maffei,
"A Systematic Literature Review on Combinations of Industry 4.0 and Lean Production,"
i 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, s. 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, s. 1072-1080, 2023.
[26]
N. Rea Minango och A. Maffei,
"Using physical interfaces for product design: from design to assembly planning,"
i Procedia CIRP, 56th CIRP International Conference on Manufacturing Systems, CIRP CMS 2023, 2023, s. 1303-1308.
[27]
F. M. Monetti och A. Maffei,
"Feeding-as-a-Service in a cloud manufacturing environment,"
i 56th CIRP International Conference on Manufacturing Systems, CIRP CMS 2023, 2023, s. 1387-1392.
[28]
E. Boffa och 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,"
i 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, s. 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, s. 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, s. 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,"
i 3Rd International Conference On Industry 4.0 And Smart Manufacturing, 2022, s. 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, s. 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, s. 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, s. 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, s. 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, s. 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, s. 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, s. 9057-9067, 2022.
[48]
Q. Ji et al.,
"Development of a 3D Printed Multi-Axial Force Sensor,"
i 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,"
i Advances in Production Management Systems. Smart Manufacturing and Logistics Systems : Turning Ideas into Action, 2022, s. 556-564.
[50]
N. A. Theissen et al.,
"Towards quasi-static kinematic calibration of serial articulated industrial manipulators,"
i MED 2022 30th Mediterranean Conference on Control and Automation, 2022, s. 872-877.