Start Date

2016 12:00 AM

Description

In recent decades, interdigital capacitive sensors (Figure 1) have been proposed for nondestructive measurement of material properties [1, 2]. The interdigital sensor performance is typically evaluated based on penetration depth, signal strength and sensitivity. All of these factors depend on the sensor geometry and a major goal of sensor design is to achieve the optimum balance among these characteristics. The design task is to consider the desired sensitivity of the sensor and necessary penetration depth of the sensor fields into the sample and determine the optimal design. This study analyses the relationship between the geometrical and electrical parameters of the sensor and test-piece, and the sensor performance, using a 3D Finite Element model. A functional relationship between the output capacitance, the penetration depth, and the shape parameters of the sensor is constructed with a genetic algorithm optimized back propagation (GABP) network. Experiment shows that sensors with optimized parameters can measure the permittivity of dielectric materials effectively and accurately.

This work is supported by the China Scholarship Fund (No. 201406695031).

Language

en

File Format

application/pdf

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Jan 1st, 12:00 AM

Optimization of the Coplanar Interdigital Capacitive Sensor

In recent decades, interdigital capacitive sensors (Figure 1) have been proposed for nondestructive measurement of material properties [1, 2]. The interdigital sensor performance is typically evaluated based on penetration depth, signal strength and sensitivity. All of these factors depend on the sensor geometry and a major goal of sensor design is to achieve the optimum balance among these characteristics. The design task is to consider the desired sensitivity of the sensor and necessary penetration depth of the sensor fields into the sample and determine the optimal design. This study analyses the relationship between the geometrical and electrical parameters of the sensor and test-piece, and the sensor performance, using a 3D Finite Element model. A functional relationship between the output capacitance, the penetration depth, and the shape parameters of the sensor is constructed with a genetic algorithm optimized back propagation (GABP) network. Experiment shows that sensors with optimized parameters can measure the permittivity of dielectric materials effectively and accurately.

This work is supported by the China Scholarship Fund (No. 201406695031).