Contact Tip Trajectory in Steady-State Regime Prediction Using Deep Learning for Piezoelectric Actuators
Including friction modeling in simulation can be challenging and requires time domain simulation, which is time consuming. We propose to separate the friction modelling from the FEA (Finite Element Analysis). The approach is defined for UCM (Ultrasonic Ceramic Motor) a type of piezoelectric actuator strongly relying on friction coupling between a vibrating tip and a sliding part. The tip's trajectory is studied both with and without contact. A DNN (Deep Neural Network) is trained to predict the contact trajectory from the contactless one, which can be obtained in frequency domain. Used as a complement of contact-free simulation of the UCM, the DNN can be used to predict the behavior of the complete actuator.
2-s2.0-105002378632
École Polytechnique Fédérale de Lausanne
École Polytechnique Fédérale de Lausanne
École Polytechnique Fédérale de Lausanne
École Polytechnique Fédérale de Lausanne
École Polytechnique Fédérale de Lausanne
2024
9784886864406
3475
3479
REVIEWED
EPFL
Event name | Event acronym | Event place | Event date |
ICEMS2024 | Fukuoka, Japan | 2024-11-26 - 2024-11-29 | |