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 Surface acoustic waves (SAWs) have shown great potential for developing sensors for structural health monitoring (SHM) and lab‐on‐a‐chip (LOC) applications. Existing SAW sensors mainly rely on measuring the frequency shifts of high‐frequency (e.g., >0.1 GHz) resonance peaks. This study presents frequency‐locked wireless multifunctional SAW sensors that enable multiple wireless sensing functions, including strain sensing, temperature measurement, water presence detection, and vibration sensing. These sensors leverage SAW resonators on piezoelectric chips, inductive coupling‐based wireless power transmission, and, particularly, a frequency‐locked wireless sensing mechanism that works at low frequencies (e.g., <0.1 GHz). This mechanism locks the input frequency on the slope of a sensor's reflection spectrum and monitors the reflection signal's amplitude change induced by the changes of sensing parameters. The proof‐of‐concept experiments show that these wireless sensors can operate in a low‐power active mode for on‐demand wireless strain measurement, temperature sensing, and water presence detection. Moreover, these sensors can operate in a power‐free passive mode for vibration sensing, with results that agree well with laser vibrometer measurements. It is anticipated that the designs and mechanisms of the frequency‐locked wireless SAW sensors will inspire researchers to develop future wireless multifunctional sensors for SHM and LOC applications.
Suspension design is one of the important parts in the research field on lunar rover mobile system. To conduct detailed dynamic analysis on the new type of suspension, this paper presents a new type of six link double ring lunar rover suspension model based on ADAMS virtual simulation software. And , this paper designs the lunar rover path tracking neural network controller. Simulation and test results show that the new lunar rover suspension has strong ground adaptability, obstacle surmounting capability and anti-overturning ability compared to classic suspension, and the neural network controller based on the new suspension has good tracking ability. The research results provide a reference for autonomous navigation control on lunar rover.
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.
It is well-known that optimizing the wheel system of lunar rovers is essential. However, this is a difficult task due to the complex terrain of the moon and limited resources onboard lunar rovers. In this study, an experimental prototype was set up to analyze the existing mechanical design of a lunar rover and improve its performance. First, a new vane-telescopic walking wheel was proposed for the lunar rover with a positive and negative quadrangle suspension, considering the complex terrain of the moon. Next, the performance was optimized under the limitations of preserving the slope passage and minimizing power consumption. This was achieved via analysis of the wheel force during movement. Finally, the effectiveness of the proposed method was demonstrated by several simulation experiments. The newly designed wheel can protrude on demand and reduce energy consumption; it can be used as a reference for lunar rover development engineering in China.
Tweezers based on optical, electric, magnetic, and acoustic fields have shown great potential for contactless object manipulation. However, current tweezers designed for manipulating millimeter-sized objects such as droplets, particles, and small animals, exhibit limitations in translation resolution, range, and path complexity. Here, we introduce a novel acoustic vortex tweezers system, which leverages a unique airborne acoustic vortex end effector integrated with a three degree-of-freedom (DoF) linear motion stage, for enabling contactless, multi-mode, programmable manipulation of millimeter-sized objects. The acoustic vortex end effector utilizes a cascaded circular acoustic array, which is portable and battery-powered, to generate an acoustic vortex with a ring-shaped energy pattern. The vortex applies acoustic radiation forces to trap and spin an object at its center, simultaneously protecting this object by repelling other materials away with its high-energy ring. Moreover, our vortex tweezers system facilitates contactless, multi-mode, programmable object surfing, as demonstrated in experiments involving trapping, repelling, and spinning particles, translating particles along complex paths, guiding particles around barriers, translating and rotating droplets containing zebrafish larvae, and merging droplets. With these capabilities, we anticipate that our tweezers system will become a valuable tool for the automated, contactless handling of droplets, particles, and bio-samples in biomedical and biochemical research.
Abstract Characterizing the mechanical properties of viscoelastic materials is critical in biomedical applications such as detecting breast cancer, skin diseases, myocardial diseases, and hepatic fibrosis. Current methods lack the consideration of dispersion curves that depend on material properties and shear wave frequency. This paper presents a novel method that combines noncontact shear wave sensing and dispersion analysis to characterize the mechanical properties of viscoelastic materials. Our shear wave sensing system uses a piezoelectric stack (PZT stack) to generate shear waves and a laser Doppler vibrometer (LDV) integrated with a 3D robotic stage to acquire time-space wavefields. Next, an inverse method is employed for the wavefield analysis. This method leverages multi-dimensional Fourier transform and frequency-wavenumber dispersion curve regression. Through proof-of-concept experiments, our sensing system successfully generated shear waves and acquired its timespace wavefield in a customized viscoelastic phantom. After dispersion curve analysis, we successfully characterized two material properties (shear elasticity and shear viscosity) and measured shear wave velocities at different frequencies.
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