Inverse characterization of the orthotropic and viscoelastic material properties of lightweight plates using full field guided wave propagation data

With the advent of the 21st century, climate change-related issues began to play a crucial role in consumer decisions as well as in company objectives. To reduce carbon dioxide emission rates and increase sustainability, companies started to invest more in the development and implementation of advanced materials. Nowadays, lightweight materials such as fiber reinforced polymer laminates and wooden panels offer an alternative for metallic structures in different industrial applications ranging from automotive to construction engineering. Apart from achieving a high structural performance, these materials offer design flexibility which help to achieve the company's future goals. However, it is important to recap the fact that "everything comes with a price". On the downside, inherent anisotropy and heterogeneity of multilayered materials lead to complex mechanical behavior, whereas the low mass of certain new engineering materials results in poor acoustical isolation. Moreover, the combination of fibers with epoxy, or wood with glue, leads to appreciable viscoelastic material behavior, which complicates the design and modelling of constructions having proper structural and acoustical characteristics. The doctoral research work presented here aspired to develop a novel nondestructive method that can be employed for anisotropic mechanical parameter inversion of viscoelastic multi-layer lightweight panels. To achieve this goal, the limitations and challenges involved in traditional characterization methods were first reviewed. With this knowledge in mind, an advanced and novel inversion method based on full-field Lamb waves was introduced. Whereas the use of Lamb waves to characterize viscoelastic material properties has been proposed and studied for almost three decades, past experimental hardware limitations held back the measurement of full-field wave propagation field, which is considered to be essential to identify the anisotropic behavior of materials. Thanks to recent technological progress leading to the introduction of 3D scanning laser Doppler vibrometers (SLDV), high resolution and high-quality information can now be acquired about both in-plane and out-of-plane velocity components of the measured surfaces. In the present work, it is shown that this full wavefield propagation information obtained from SLDV measurements is fundamental to invert more accurate and robust values for the viscoelastic stiffness parameters. Following the introduction of the problem and the state-of-the-art of the considered research domain, several advanced numerical models and global optimization methods were introduced and reviewed in view of developing a more accurate and robust inversion method to overcome the limitations of the traditional methods. Next, a novel two-stage inversion algorithm, employing multi-angle information of the dispersive behavior, was proposed. The inversion method was subsequently verified using the input of various numerical studies as well as experiments. Experiments were conducted on different lightweight materials including aluminum, PMMA, carbon and glass fiber reinforced polymer, porcelain stoneware, as well as medium- and highdensity wooden fiberboard plates. The orthotropic stiffness tensor of these viscoelastic materials was obtained with high accuracy and validated with literature results. In order to extend the proposed characterization technique for quasi-real time application, machine learning algorithms were investigated in the last part of this thesis. The accuracy of the inverted stiffness parameters has been reviewed for different material symmetry groups, and the effect of noise on the machine learning algorithms was studied as well. The results confirms that the developed machine learning-based inversion method has the potential to characterize materials in quasi-real time, which is a persuasive benefit for industrial applications.

Jaar van publicatie:2023

Toegankelijkheid:Closed