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Abstract Thermoset composites, utilized in additive manufacturing, are distinguished by their excellent thermal and mechanical properties, enabling them to maintain structural integrity even under high-temperature conditions. An accurate method for characterizing the mechanical properties is necessary to ensure the performance parameters, reliability, and safety of materials during and post-manufacturing. However, characterizing 3D-printed thermoset composites is challenging due to the anisotropy introduced by the additive manufacturing process and factors such as delamination and porosity. This also leads to difficulties in accurately characterizing composites with traditional testing methods. To address this, this paper introduces a novel method that combines a non-destructive Piezoelectric transducer-laser Doppler Vibrometer (PZT-LDV) guided wave sensing system with an optimization algorithm-enhanced wavenumber analysis technique. A series of experiments were conducted to validate the concept of measuring the mechanical properties of a 3D-printed thermoset material panel. Our method successfully determined two material properties — shear wave speed and Poisson’s ratio in multiple directions on the test panel. This study aims to establish a precise and rapid non-destructive testing method that can effectively characterize various composite materials and monitor their performance throughout the additive manufacturing process. 

Abstract Thermoset materials have begun to be applied in additive composite manufacturing due to their ability to withstand high temperatures without losing structural integrity. Meanwhile, the characterization of mechanical properties for additively manufactured composites is critical for ensuring material reliability and safety. However, traditional testing methods struggle to accurately and nondestructively characterize additively manufactured composites due to challenges posed by curing processes, microstructural variability, anisotropic properties of thermoset composites, and the risk of damaging these materials during evaluation. For characterizing the mechanical properties of additive-manufactured thermoset composites, this paper presents a novel method that combines a nondestructive PZT-LDV guided wave sensing system and a wavenumber analysis that fuses multidimensional Fourier transform with dispersion curve regression. For proof of concept, we performed an experiment using our method to measure a 3D-printed thermoset composite panel. Based on our nondestructive approach, two material properties (shear wave velocity and Poisson’s ratio) in multiple directions were successfully determined for the tested panel. We expect this research to introduce a non-contact and efficient method for characterizing various composites and monitoring their property changes after additive manufacturing. 

aser Doppler vibrometry and wavefield analysis have recently shown great potential for nondestructive evaluation, structural health monitoring, and studying wave physics. However, there are limited studies on these approaches for viscoelastic soft materials, especially, very few studies on the laser Doppler vibrometer (LDV)-based acquisition of time–space wavefields of dispersive shear waves in viscoelastic materials and the analysis of these wavefields for characterizing shear wave dispersion and evaluating local viscoelastic property distributions. Therefore, this research focuses on developing a piezo stack-LDV system and shear wave time–space wavefield analysis methods for enabling the functions of characterizing the shear wave dispersion and the distributions of local viscoelastic material properties. Our system leverages a piezo stack to generate shear waves in viscoelastic materials and an LDV to acquire time–space wavefields. We introduced space-frequency-wavenumber analysis and least square regression-based dispersion comparison to analyze shear wave time–space wavefields and offer functions including extracting shear wave dispersion relations from wavefields and characterizing the spatial distributions of local wavenumbers and viscoelastic properties (e.g., shear elasticity and viscosity). Proof-of-concept experiments were performed using a synthetic gelatin phantom. The results show that our system can successfully generate shear waves and acquire time–space wavefields. They also prove that our wavefield analysis methods can reveal the shear wave dispersion relation and show the spatial distributions of local wavenumbers and viscoelastic properties. We expect this research to benefit engineering and biomedical research communities and inspire researchers interested in developing shear wave-based technologies for characterizing viscoelastic materials.