Fig. 3: Predictive capabilities of the multi-physical in-silico platform. | Nature Communications

Fig. 3: Predictive capabilities of the multi-physical in-silico platform.

From: In-silico platform for the multifunctional design of 3D printed conductive components

Fig. 3: Predictive capabilities of the multi-physical in-silico platform.The alt text for this image may have been generated using AI.

a Simulations of thermo-mechanical tests at a temperature of 45 °C on longitudinal, transverse and oblique samples. b Simulations of mechano-electrical tests on longitudinal, transverse and oblique samples. c Simulations of electro-thermal tests applying an electric field (\({\mathbb{E}}\)) of 250 V/m on longitudinal, transverse and oblique samples. d Simulation of a thermo-electro-mechanical test applying an electric field (\({\mathbb{E}}\)) of 187.5 V/m and uniaxial tensile loading on a longitudinal sample. Experimental (black) and numerical (blue/green/red depending on the printing orientation) results are presented together in all graphs. The grey shaded areas represent the experimental deviation of the tests. To provide fair comparisons between experimental and modelling data, as the voltage at reference configuration is kept constant during the tests, the effective resistivity and electric field are calculated in the reference configuration too.

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