In this paper, a novel technique for the viscoelastic characterization of biosamples is presented. The measuring tool consists of MEMS-technology based tweezers that are used, in general, to perform micromanipulation tasks. A mechanical model is developed for the nonlinear dynamics of the microsystem, composed of the tweezers and of the sample to be analyzed. The Maxwell liquid drop constitutive law is considered for the sample. The identification of the viscoelastic parameters is performed by implementing a genetic algorithm.

Verotti, M., Di Giamberardino, P., Belfiore, N.P., Giannini, O. (2020). A genetic algorithm for the estimation of viscoelastic parameters of biological samples manipulated by mems tweezers. In Lecture Notes in Mechanical Engineering (pp.920-931). Springer [10.1007/978-3-030-41057-5_75].

A genetic algorithm for the estimation of viscoelastic parameters of biological samples manipulated by mems tweezers

Belfiore N. P.;
2020-01-01

Abstract

In this paper, a novel technique for the viscoelastic characterization of biosamples is presented. The measuring tool consists of MEMS-technology based tweezers that are used, in general, to perform micromanipulation tasks. A mechanical model is developed for the nonlinear dynamics of the microsystem, composed of the tweezers and of the sample to be analyzed. The Maxwell liquid drop constitutive law is considered for the sample. The identification of the viscoelastic parameters is performed by implementing a genetic algorithm.
978-3-030-41056-8
Verotti, M., Di Giamberardino, P., Belfiore, N.P., Giannini, O. (2020). A genetic algorithm for the estimation of viscoelastic parameters of biological samples manipulated by mems tweezers. In Lecture Notes in Mechanical Engineering (pp.920-931). Springer [10.1007/978-3-030-41057-5_75].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11590/386071
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