Performance Evaluation of Acoustic Metamaterial Developed Using 3D Printing Process

Abstract

This thesis presents the design, development and characterization of 3D printed acoustic metamaterials. Traditional sound-absorbing materials require substantial space and offer limited control over targeted frequencies. To address this challenge, novel metamaterial structures, including tetrakaidecahedron, DENORMS (Designs for Noise Reducing Materials and Structures) cells, and re-entrant auxetic cell based acoustic metamaterials, were developed and analysed. The metamaterials were modelled using a CAD package and fabricated via Digital Light Processing (DLP) 3D printing technique. The DLP technique was used for the fabrication of these metamaterial due to its high resolution and highly flowable liquid resins. The findings underscore the potential of 3D-printed acoustic metamaterials in noise control applications, demonstrating their tunability through geometric modifications. The 3D printed samples were put under a microscope to check for its dimensional accuracy. Their acoustic absorption properties were investigated through thermoviscous numerical simulations and validated through experimental testing using an impedance tube (two-microphone method). The influence of geometric parameters such as strut thickness, cylindrical and spherical diameters, vertical and inclined strut lengths, and the number of cells on the absorption coefficient was systematically studied. The results demonstrated that increasing the number of unit cells enhanced absorption and shifted the frequency response to lower ranges. The effect of changing the geometric parameters have been studied in detail for the various Additionally, soft computing approach to predict the sound absorption coefficient of the acoustic metamaterial has been presented in this study. Various machine learning techniques like Neural Networks (NN), Random Forests (RF), Decision Trees (Rpart), and Generalized Linear Models (GLM) were employed to develop predictive models for absorption coefficients. These models proved effective in forecasting acoustic performance with higher computational efficiency compared to numerical simulations. During the literature review it was observed that there was a lack of analysis of the combined effect of several parameters on the sound absorption coefficient of the metamaterials. The effect of geometric parameters on the absorption coefficient had been considered in an isolated way i.e. One Factor at a Time. To overcome these gaps, an experimental study based on Design of Experiments (DOE) on the acoustic metamaterial was conducted to understand the effects of geometric parameters and frequency on the sound absorption coefficient. The absorption coefficient data was used to build the RSM based statistical model. Pareto chart, main effect plot, interaction chart and response surface plots were used to understand the effect and interaction of various input variables on the sound absorption coefficient. Further, the developed statistical model was used to determine the optimal input values for the maximized sound absorption. Comparative analysis of different metamaterial configurations revealed that re-entrant auxetic cells exhibited superior low-frequency absorption, with a peak absorption coefficient of approximately 0.9 at 1000 Hz, outperforming the DENORMS and tetrakaidecahedron-based metamaterials. Further comparison with existing periodic resonator structures and cylindrical rod microstructures confirmed that the developed metamaterials achieved enhanced low-frequency absorption with reduced sample thickness.