His scientific interests lie mostly in Robot, Artificial intelligence, Control engineering, Manufacturing engineering and Machine tool. His research integrates issues of Systems engineering, Process and Machining in his study of Robot. Alexander Verl is studying Robotics, which is a component of Artificial intelligence.
Alexander Verl performs integrative study on Robotics and Flexibility in his works. His work deals with themes such as Control, Model predictive control, Parallel manipulator and Electric motor, which intersect with Control engineering. His Machine tool study incorporates themes from Preventive maintenance, Condition monitoring and Predictive maintenance.
The scientist’s investigation covers issues in Robot, Control engineering, Artificial intelligence, Machine tool and Simulation. His Robot research integrates issues from Process and Machining. His studies in Process integrate themes in fields like Quality, Automation, Manufacturing engineering, Systems engineering and Modular design.
The concepts of his Control engineering study are interwoven with issues in Control system, Control theory, Process, Parallel manipulator and Control. Alexander Verl studies Robotics, a branch of Artificial intelligence. His studies deal with areas such as Energy consumption, Efficient energy use and Ball screw as well as Machine tool.
Alexander Verl mostly deals with Robot, Control engineering, Distributed computing, Automation and Control theory. His Robot research is multidisciplinary, relying on both Control theory and Trajectory. His Control engineering research incorporates elements of Control system, Reinforcement learning, Focus and Machining.
His Machining research includes elements of Process, Machine tool and Identification. The Automation study which covers Software that intersects with Domain, Systems engineering and Information model. His study in Bin picking and Robotics falls under the purview of Artificial intelligence.
His primary areas of investigation include Context, Distributed computing, Machining, Manufacturing engineering and Robot. His Distributed computing study combines topics in areas such as End-to-end principle, Shell, Time sensitive networking and Control. His work carried out in the field of Machining brings together such families of science as Process, Zero Defects and Identification.
His Manufacturing engineering study combines topics from a wide range of disciplines, such as Quality and Process. His study of Robotics is a part of Robot. His work on Hardware-in-the-loop simulation as part of general Control engineering research is frequently linked to Material flow, thereby connecting diverse disciplines of science.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Cooperation of human and machines in assembly lines
J. Krüger;J. Krüger;T.K. Lien;A. Verl;A. Verl.
CIRP Annals (2009)
Machine tool feed drives
Y. Altintas;A. Verl;C. Brecher;L. Uriarte.
Cirp Annals-manufacturing Technology (2011)
Making existing production systems Industry 4.0-ready
Jan Schlechtendahl;Matthias Keinert;Felix Kretschmer;Armin Lechler.
Production Engineering (2015)
A generic energy consumption model for decision making and energy efficiency optimisation in manufacturing
Anton Dietmair;Alexander Verl.
International Journal of Sustainable Engineering (2009)
Care-O-bot ® 3 - creating a product vision for service robot applications by integrating design and technology
Ulrich Reiser;Christian Connette;Jan Fischer;Jens Kubacki.
intelligent robots and systems (2009)
Grasping devices and methods in automated production processes
Gualtiero Fantoni;Marco Santochi;Gino Dini;Kirsten Tracht.
Cirp Annals-manufacturing Technology (2014)
IPAnema: A family of Cable-Driven Parallel Robots for Industrial Applications
Andreas Pott;Hendrick Mütherich;Werner Kraus;Valentine Schmidt.
The Bionic Handling Assistant: a success story of additive manufacturing
Andrzej Grzesiak;Ralf Becker;Alexander Verl.
Assembly Automation (2011)
Energy consumption modeling and optimization for production machines
A. Dietmair;A. Verl.
ieee international conference on sustainable energy technologies (2008)
Improving robotic machining accuracy through experimental error investigation and modular compensation
Ulrich Schneider;Manuel Drust;Matteo Ansaloni;Christian Lehmann.
The International Journal of Advanced Manufacturing Technology (2016)
Profile was last updated on December 6th, 2021.
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