Matching frequency response measurements and reduced order models for the inverse identification of viscoelastic properties

TL;DR

This paper presents a method for identifying viscoelastic properties of 3D-printed materials using reduced-order models and particle swarm optimization, validated with experimental frequency response data.

Contribution

It introduces a practical inverse identification approach for viscoelastic properties that accounts for experimental challenges and validates it on real materials.

Findings

Successful identification of POM and ceramic materials' properties

Method accurately fits frequency response functions with uncertainty quantification

Applicable despite experimental noise and boundary condition issues

Abstract

3D-printed materials are used in many different industries (automotive, aviation, medicine, etc.). Most of these 3D-printed materials are based on ceramics or polymers whose mechanical properties vary with frequency. For numerical modeling, it is crucial to characterize this frequency dependency accurately to enable realistic finite-element simulations. At the same time, the damping behavior plays a key role in product development, since it governs a component's response at resonance and thus impacts both performance and longevity. In current research, inverse material characterization methods are getting more and more popular. However, their practical validation and applicability on real measurement data have not yet been discussed widely. In this work, we show the identification of two different materials, POM and additively manufactured sintered ceramics, and validate it with experimental data of a well-established measurement technique (dynamic mechanical analysis). The material identification process considers state-of-the-art reduced-order modeling and constrained particle swarm optimization, which are used to fit the frequency response functions of point measurements obtained by a laser Doppler vibrometer. This work shows the quality of the method in identifying the parameters defining the viscoelastic fractional derivative model, including their uncertainty. It also illustrates the applicability of this identification method in the presence of practical difficulties that come along with experimental data such as boundary conditions and noise.