Characterizing the Electrical Properties of Anisotropic, 3D-Printed Conductive Sheets for Sensor Applications

Dijkshoorn, Alexander; Schouten, Martijn; Wolterink, Gerjan; Sanders, Remco G.P.; Krijnen, Gijs

doi:10.1109/jsen.2020.3007249

Cited by 21 publications

(49 citation statements)

References 22 publications

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“…The structures are simulated by the Finite Element Method (FEM) using the Electric Currents module of COMSOL, following the methodology presented in [ 42 ]. The voltages, currents, and power dissipation density, as well as the total impedance, are simulated.…”

Section: Methodsmentioning

confidence: 99%

“…The mathematical model can be derived by considering the distributed electrical properties of the traxels and the contacts between the traxels, being represented by an equivalent network, as shown in Figure 6 , and as we used before for the FEM simulations shown in [ 42 ].…”

Section: Theoretical Modelmentioning

confidence: 99%

“…Finally, the average dissipated power is important for the experimental validation presented in [ 42 ], and it can be expressed as (with * the complex conjugated):

…”

Section: Theoretical Modelmentioning

confidence: 99%

“…Modelling and characterization methods for studying anisotropic electrical conduction are required to understand and predict the performance of 3D-printed sensors [ 42 ] because the electrical anisotropic properties also influence the performance of 3D-printed sensors [ 36 , 41 ].…”

Section: Introductionmentioning

confidence: 99%

“…Because voids, raster orientation, and bonding are important for the anisotropic conductivity [ 41 ], these features need to be included in the modeling. In previous work, the authors also used FEM simulations based on the model of Hampel et al to simulate the in-plane anisotropy by including the inter-traxel resistance and meanders [ 42 ]. However, homogenized models cannot include local features, electrical network models do not present interpretable results with high resolution and simplified (1D) analytical models, and numerical and FEM simulations lack insightful information regarding anisotropic conduction.…”

Section: Introductionmentioning

confidence: 99%

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Modelling of Anisotropic Electrical Conduction in Layered Structures 3D-Printed with Fused Deposition Modelling

Dijkshoorn

Schouten

Krijnen

2021

Sensors

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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.

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Section: Methodsmentioning

confidence: 99%

Section: Theoretical Modelmentioning

confidence: 99%

“…Finally, the average dissipated power is important for the experimental validation presented in [ 42 ], and it can be expressed as (with * the complex conjugated):

…”

Section: Theoretical Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Modelling of Anisotropic Electrical Conduction in Layered Structures 3D-Printed with Fused Deposition Modelling

Dijkshoorn

Schouten

Krijnen

2021

Sensors

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Single‐Process 3D‐Printed Triaxial Accelerometer

Arh

Slavič

2021

Adv Materials Technologies

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For thermoplastic material extrusion 3D‐printing functional filaments with electrically conductive, piezoresistive, piezoelectric, capacitive or magnetic properties are developed. The functional filaments result in piezoresisitve static/quasi‐static sensors; however, these 3D‐printed sensors are manufactured in several processes, for example, the creation of highly conductive paths using embedded copper wires or by silver inking. The multi‐process 3D printing of sensors presents an obstacle to smart functional 3D‐printed structures where the sensory element is in situ printed at the location and orientation of use, including the electrical paths. This research introduces a three axial piezoresistive accelerometer where the whole sensor, including highly conductive electrical paths, is printed in the same process of thermoplastic material extrusion. The structural components of the sensor are printed with a non‐conductive polylactide material, the sensory element with a conductive material that has a relatively high resistivity, and the electrical paths with a conductive material that has a relatively low resistivity. As discussed in the manuscript with single‐process sensors, the functional tuning of sensors is as easy as changing the design. The design and manufacturing solutions researched for the triaxial accelerometer can be applied to other 3D‐printed sensors, for example, force sensors and opens up the possibility of a single‐process 3D‐printed smart structure.

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