In this review paper, we focus on the 3D printing technologies that consist of the extruding of fluid material in lines to form structures for electro- and biomechanical applications. Our paper reviews various 3D print technologies, materials, sensing technologies and applications of extrusion-based 3D printing. We also discuss how to overcome some of the challenges with 3D printed sensors, such as the anisotropy of the conductors as well as the drift and nonlinearity of the materials.

3D-printing conductive structures have recently been receiving increased attention, especially in the field of 3D-printed sensors. However, the printing processes introduce anisotropic electrical properties due to the infill and bonding conditions. Insights into the electrical conduction that results from the anisotropic electrical properties are currently limited. Therefore, this research focuses on analytically modeling the electrical conduction. The electrical properties are described as an electrical network with bulk and contact properties in and between neighbouring printed track elements or traxels. The model studies both meandering and open-ended traxels through the application of the corresponding boundary conditions. The model equations are solved as an eigenvalue problem, yielding the voltage, current density, and power dissipation density for every position in every traxel. A simplified analytical example and Finite Element Method simulations verify the model, which depict good correspondence. The main errors found are due to the limitations of the model with regards to 2D-conduction in traxels and neglecting the resistance of meandering ends. Three dimensionless numbers are introduced for the verification and analysis: the anisotropy ratio, the aspect ratio, and the number of traxels. Conductive behavior between completely isotropic and completely anisotropic can be modeled, depending on the dimensionless properties. Furthermore, this model can be used to explain the properties of certain 3D-printed sensor structures, like constriction-resistive strain sensors.

This research explores the feasibility of combining surface electromyography (sEMG) and multi-frequency electrical impedance myography (mfEIM) for predicting joint angles and torques. Methods: We utilize a current-limited multi-frequency electrical impedance spectrometer to simultaneously perform mfEIM on the biceps brachii and triceps brachii at 14 frequencies. The same system also measures the sEMG signals using the same electrodes. Measurements are conducted while subjects perform tasks in a one-degree-of-freedom exoskeleton, which enables the measurement of elbow joint torque and angle. We train time-delay neural networks to model the relationships between mfEIM, sEMG, joint torque, and joint angle. Results and Conclusions: The results demonstrate that the sEMG signal can predict joint torque and that mfEIM can predict joint angle. Additionally, we find indications that a combination of sEMG and mfEIM enhances joint torque predictions compared to using sEMG alone. We also determine the delays between the sEMG, mfEIM signals, and the joint angle and torque, finding that the joint torque signal has a delay relative to the sEMG signal of 155ms during an isometric exercise and 125ms during dynamic tasks. Significance: These results suggest that combining sEMG and mfEIM measurements could provide additional insights during biomedical experiments.

Current force sensors used to capture fingertip interaction forces lack compliance to the fingertip tissue resulting in the loss of touch sensation of the user. 3D printing offers the possibility to create personalized soft sensing structures. This work evaluates a 3D printed soft sensor that measures normal and shear interactions forces based on the deformations of the thumb and index fingertips of 7 subjects using an instrumented object. Due to the use of (carbon doped) thermoplastic materials, the signals provided by these sensing structures suffer from nonlinearities. Therefore, two compensation models, based on a neural network and recurrent neural network analogous to an electrical model are used to compensate for the nonlinear effects. The performance of the sensors was analysed using the normalized cross-correlation and the root-mean-square error. The output of the force sensors are highly correlated with the applied shear and normal force components. When paired with compensation models the correlation and error of the sensor output can be further improved. These results indicate that the proposed flexible fingertip interaction force sensors have a high potential for future applications.

Electrohydrodynamic (EHD) pumps noiselessly generate fluid flow in dielectric liquids using high electric fields to create, accelerate, and neutralize ions. Such pumps find applications in wearable actuators, soft robotics, and active thermal management. The influence of fluid properties on pressure and flow rate remains poorly understood. We present a systematic comparison of EHD pumping across 11 fluids, including 8 previously unidentified candidates, spanning viscosities from 0.5 to 19 millipascal seconds and dielectric constants from 2.3 to 64. Tests with more than 30 flexible fiber pumps show that low-viscosity and high–dielectric constant liquids markedly enhance pumping performance. For 1.2-millimeter–inner-diameter fiber pumps, replacing the commonly used Novec 7100 with propylene carbonate increased fluidic power density fivefold, reaching 495 milliwatts per cubic centimeter at 4.4 kilovolts. This study identifies key fluid properties for pumping, expands the EHD fluid library, and establishes a rigorous benchmark for performance evaluation, providing guidance for future EHD pump designs.