Search for: All records

Abstract The advancement of additive manufacturing has significantly transformed the production process of metal components. However, the unique challenges associated with layer-by-layer manufacturing result in anisotropy in the microstructure and uneven mechanical properties of additive-manufactured metal products. Traditional testing methods often fall short of providing the precise mechanical performance evaluations required to meet industry standards. This paper introduces an innovative approach that combines a nondestructive Lamb wave sensing system with a wavenumber analysis method to characterize the mechanical properties of 3D-printed metal panels in multiple directions. Our method employs piezoelectric actuators (PZT) to generate Lamb waves and utilizes a laser Doppler vibrometer (LDV) for non-contact, two-dimensional grid acquisition of the wavefield. The anisotropic properties of the metal 3D-printed structure will be captured in the wavefield, offering an informative dataset for wavenumber analysis. The proposed analytical method includes multi-directional frequency wavenumber analysis and a least-squares-based dispersion curves regression. The integration of the above advanced analytical tools allows for the accurate characterization of the shear wave velocity and Poisson’s ratio of the plate structure. This precise characterization is crucial for ensuring the structural integrity and consistent mechanical properties of 3D-printed metal components. We validated our method using a 3D-printed stainless-steel plate, demonstrating its capability to effectively characterize the multi-directional mechanical properties of additively manufactured metal plates. We expect that our method can provide a nondestructive, time-efficient, and comprehensive quality control solution for additive manufacturing across various industries. 

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.