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Influence of the suspension shape on the dynamic characteristics of the electrodynamic shakers

Augustyn Damian, Fidali Marek

Vibrations in Physical Systems

2023

Vol. 34, nr 2

art. no. 2023227

Electrodynamic shakers are being used for years in dynamic tests of machines and devices. One of the main components of electrodynamic shakers is the armature suspension. Suspension is one of the factors responsible for the dynamic characteristics of the shaker. Various types of suspensions are used in shakers [1, 2]. Their role is to ensure the correct stroke of the armature and to keep the coil concentrically in the magnetic gap. As part of the research, the influence of the shape of the suspension springs of the electrodynamic shaker on its dynamic characteristics was evaluated. During the research, self-designed electrodynamic shaker was used, in which a suspension consisting of two-disc springs made of glass fiber with a thickness of 0.5 mm was used. Eight different spring types were prepared for the study. The tests were carried out in laboratory conditions on a previously prepared test bench. For each of the springs tested, dynamic frequency characteristics were determined for three frequency ranges: 10 - 100 Hz with a step of 10 Hz, 100 Hz - 1000 Hz and 1000 Hz - 10000 Hz with a step of 100 Hz. Studies show a significant influence of the shape of the springs used on the dynamic characteristics of the modal shaker. For the preselected springs, tests were carried out without and with a load. The characteristics determined during the research were analyzed, which allowed to indicate the optimal shape of the spring, due to the values of the generated force and the linearity of the dynamic characteristics [3, 4].

Human-induced force reconstruction using a non-linear electrodynamic shaker applying an iterative neural network algorithm

Peláez-Rodríguez César, Magdaleno Álvaro, Salcedo-Sanz Sancho, Lorenzana Antolín

Bulletin of the Polish Academy of Sciences. Technical Sciences

2023

Vol. 71, nr 3

art. no. e144615

An iterative neural network framework is proposed in this paper for the human-induced Ground Reaction Forces (GRF) replication with an inertial electrodynamic mass actuator (APS 400). This is a first approach to the systematization of dynamic load tests on structures in a purely objective, repeatable and pedestrian-independent basis. Therefore, an inversion-free offline algorithm based on Machine Learning techniques has been applied for the first time on an electrodynamic shaker, without requiring its inverse model to tackle the inverse problem of successful force reconstruction. The proposed approach aims to obtain the optimal drive signal to minimize the error between the experimental shaker output and the reference force signal, measured with a pair of instrumented insoles (Loadsol©) for human bouncing at different fre- quencies and amplitudes. The optimal performance, stability and convergence of the system are verified through experimental tests, achieving excellent results in both time and frequency domain.