Geometry Transfer Using Cross Sections for Sequential Mechanical–Electrostatic Simulation of Microstructured Flexible Capacitive Pressure Sensors

Predicting how microstructure dimensions affect the response of flexible capacitive pressure sensors can guide sensor design. Large deformation and contact, however, complicate the construction of dielectric and air domains from the deformed mechanical geometry. This study presents a geometry transfer method based on equivalent cross sections. The method uses the finite element software packages Abaqus 2023 and COMSOL Multiphysics 6.2 for mechanical and electrostatic analysis, respectively. The electrostatic domains are reconstructed from horizontal section areas of the deformed boundary, current polydimethylsiloxane layer thicknesses and microstructure height. The method was evaluated using two square frustum geometries and measurements from three independent devices. Five equivalent square sections yielded volume meshes and capacitance solutions for all 29 selected states of each geometry. For the baseline geometry, capacitance differed from a 17-section reference by at most 0.1092% across four states spanning 0–411.85 kPa. Replacing equivalent squares with fitted contours of equal area changed capacitance by at most 0.04835%. A microscopy-guided geometry adjustment preserved the initial electrode separation and sidewall angle. Over 0–350 kPa, the adjustment reduced the root mean square error between the predicted relative capacitance response and the experimental mean from 0.0721 to 0.0292. The method provides a practical route from nonlinear deformation to capacitance prediction for microstructure design.

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Publication Details

Journal
Micromachines
Published
2026-10-14
DOI
https://doi.org/10.3390/mi17101190
Primary Topic
Advanced Sensor and Energy Harvesting Materials
Type
article
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article

Geometry Transfer Using Cross Sections for Sequential Mechanical–Electrostatic Simulation of Microstructured Flexible Capacitive Pressure Sensors

Qi Zhang, Yiwei Shao, Kangping Xiao, Xiaoya Liang et al.
Micromachines
Advanced Sensor and Energy Harvesting Materials
article

Geometry Transfer Using Cross Sections for Sequential Mechanical–Electrostatic Simulation of Microstructured Flexible Capacitive Pressure Sensors

Qi Zhang, Yiwei Shao, Kangping Xiao, Xiaoya Liang, Xinyu Zhang, Wenzhe Bi, Yulong Zhao
article en

Abstract

Predicting how microstructure dimensions affect the response of flexible capacitive pressure sensors can guide sensor design. Large deformation and contact, however, complicate the construction of dielectric and air domains from the deformed mechanical geometry. This study presents a geometry transfer method based on equivalent cross sections. The method uses the finite element software packages Abaqus 2023 and COMSOL Multiphysics 6.2 for mechanical and electrostatic analysis, respectively. The electrostatic domains are reconstructed from horizontal section areas of the deformed boundary, current polydimethylsiloxane layer thicknesses and microstructure height. The method was evaluated using two square frustum geometries and measurements from three independent devices. Five equivalent square sections yielded volume meshes and capacitance solutions for all 29 selected states of each geometry. For the baseline geometry, capacitance differed from a 17-section reference by at most 0.1092% across four states spanning 0–411.85 kPa. Replacing equivalent squares with fitted contours of equal area changed capacitance by at most 0.04835%. A microscopy-guided geometry adjustment preserved the initial electrode separation and sidewall angle. Over 0–350 kPa, the adjustment reduced the root mean square error between the predicted relative capacitance response and the experimental mean from 0.0721 to 0.0292. The method provides a practical route from nonlinear deformation to capacitance prediction for microstructure design.

MicromachinesVol. 17(10)
State Key Laboratory of Mechanical Manufacturing System Engineering (CN), Xi'an Jiaotong University (CN)
Openalex Percentile: Top 24%
Advanced Sensor and Energy Harvesting Materials
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