Tom Oomen

Tom Oomen

Title: From data to future high-tech systems

Abstract:

The future of manufacturing equipment and scientific instruments hinges on the ability to perform precise and fast motions. Examples of such mechatronic systems include wafer scanners, printing systems, pick-and-place machines, microscopes, and telescopes. These high-tech systems are subject to ever-increasing speed, accuracy, and flexibility requirements. We envisage revolutionary data-intensive mechatronic designs with a massive number of actuators and sensors. These future systems can autonomously improve their performance through self-learning and self-healing. These ideas are illustrated in industrial case-studies. 

Affiliation: Professor, Eindhoven University of Technology, Department of Mechanical Engineering, Eindhoven, The Netherlands, and Professor, Delft University of Technology, Faculty of Mechanical Engineering, Delft, The Netherlands

Bio: Tom Oomen is full professor with the Department of Mechanical Engineering at the Eindhoven University of Technology. He is also a part-time full professor with the Delft University of Technology. He received the M.Sc. degree (cum laude) and Ph.D. degree from the Eindhoven University of Technology, Eindhoven, The Netherlands. He held visiting positions at KTH, Stockholm, Sweden, and at The University of Newcastle, Australia. He is a recipient of the 7th Grand Nagamori Award, the Corus Young Talent Graduation Award, the IFAC 2019 TC 4.2 Mechatronics Young Research Award, the 2015 IEEE Transactions on Control Systems Technology Outstanding Paper Award, the 2017 IFAC Mechatronics Best Paper Award, the 2019 IEEJ Journal of Industry Applications Best Paper Award, and recipient of a Veni and Vidi personal grant. He is currently a Senior Editor of IEEE Control Systems Letters (L-CSS) and Co-Editor-in-Chief of IFAC Mechatronics, and he has served on the editorial board of IEEE Transactions on Control Systems Technology. He has also been vice-chair for IFAC TC 4.2 and a member of the Eindhoven Young Academy of Engineering. His research interests are in the field of data-driven modeling, learning, and control, with applications in precision mechatronics.