Niels Lohse
Niels Lohse
Professor of Manufacturing Automation and Robotics, School of Mechanical, Electrical and
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Cited by
Cited by
An investigation into minimising total energy consumption and total weighted tardiness in job shops
Y Liu, H Dong, N Lohse, S Petrovic, N Gindy
Journal of Cleaner Production 65, 87-96, 2014
Bending angle prediction and control of soft pneumatic actuators with embedded flex sensors–a data-driven approach
K Elgeneidy, N Lohse, M Jackson
Mechatronics 50, 234-247, 2018
The IDEAS project: plug & produce at shop‐floor level
M Onori, N Lohse, J Barata, C Hanisch
Assembly automation 32 (2), 124-134, 2012
Soft pneumatic grippers embedded with stretchable electroadhesion
J Guo, K Elgeneidy, C Xiang, N Lohse, L Justham, J Rossiter
Smart Materials and Structures 27 (5), 055006, 2018
A multi-objective genetic algorithm for optimisation of energy consumption and shop floor production performance
Y Liu, H Dong, N Lohse, S Petrovic
International Journal of Production Economics 179, 259-272, 2016
Equipment ontology for modular reconfigurable assembly systems
N Lohse, H Hirani, S Ratchev
International journal of flexible manufacturing systems 17, 301-314, 2005
Exploring the integration of the human as a flexibility factor in CPS enabled manufacturing environments: Methodology and results
P Fantini, G Tavola, M Taisch, J Barbosa, P Leitão, Y Liu, MS Sayed, ...
IECON 2016-42nd Annual Conference of the IEEE Industrial Electronics Society …, 2016
Directly Printable Flexible Strain Sensors for Bending and Contact Feedback of Soft Actuators
K Elgeneidy, G Neumann, M Jackson, N Lohse
Frontiers in Robotics and AI 5, 2, 2018
A fuzzy-decision-tree approach for manufacturing technology selection exploiting experience-based information
L Evans, N Lohse, M Summers
Expert Systems with Applications 40 (16), 6412-6426, 2013
EEG based arm movement intention recognition towards enhanced safety in symbiotic Human-Robot Collaboration
A Buerkle, W Eaton, N Lohse, T Bamber, P Ferreira
Robotics and Computer-Integrated Manufacturing 70, 102137, 2021
Mapping industry 4.0 enabling technologies into united nations sustainability development goals
M M. Mabkhot, P Ferreira, A Maffei, P Podržaj, M Mądziel, D Antonelli, ...
Sustainability 13 (5), 2560, 2021
Reducing environmental impact of production during a rolling blackout policy–a multi-objective schedule optimisation approach
Y Liu, H Dong, N Lohse, S Petrovic
Journal of Cleaner Production 102, 418-427, 2015
An MDP model-based reinforcement learning approach for production station ramp-up optimization: Q-learning analysis
S Doltsinis, P Ferreira, N Lohse
IEEE Transactions on Systems, Man, and Cybernetics: Systems 44 (9), 1125-1138, 2014
Towards an ontology framework for the integrated design of modular assembly systems
N Lohse
University of Nottingham, 2006
An ontology for the definition and validation of assembly processes for evolvable assembly systems
N Lohse, H Hirani, S Ratchev, M Turitto
(ISATP 2005). The 6th IEEE International Symposium on Assembly and Task …, 2005
Improving human robot collaboration through Force/Torque based learning for object manipulation
A Al-Yacoub, YC Zhao, W Eaton, YM Goh, N Lohse
Robotics and Computer-Integrated Manufacturing 69, 102111, 2021
Energy-and labor-aware production scheduling for industrial demand response using adaptive multiobjective memetic algorithm
X Gong, Y Liu, N Lohse, T De Pessemier, L Martens, W Joseph
IEEE Transactions on Industrial Informatics 15 (2), 942-953, 2018
Symbiotic Assembly Systems–A New Paradigm
P Ferreira, S Doltsinis, N Lohse
Procedia CIRP 17, 26-31, 2014
A Decision Support System for rapid ramp-up of industry 4.0 enabled production systems
S Doltsinis, P Ferreira, MM Mabkhot, N Lohse
Computers in Industry 116, 103190, 2020
Towards Web‐enabled design of modular assembly systems
N Lohse, S Ratchev, G Valtchanov
Assembly Automation 24 (3), 270-279, 2004
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