In this paper, we propose a piezoelectric actuator-sensor pair that can classify several objects. It consists of two polyvinylidene-fluoride films above a polyethylene-terephthalate substrate. Herein, the actuator is connected to an voltage supplier, and the sensor output signal is acquired through a measuring equipment. Specifically, this pair is installed on a robot hand. When the objects are grasped by the robot hand in static state, the actuator oscillates as sinusoidal input voltages with frequency sweep are applied for a few seconds. At the same time, the sensor data is obtained and undergoes preprocessing procedure for learning process. The neural network classifier model is trained by learning process. After conducting the learning process, we test the feasibility of the actuator-sensor pair by demonstrating the real-time recognition system.
Piezoelectric materials have found numerous applications in sensors with the characteristic of flexibility and sensitivity. Taking advantage of their characteristic, we fabricate a multi-layered cross-shaped piezoelectric sensor for torsional load analysis. It consists of a polyethylene terephthalate substrate and two piezoelectric layers placed in the crossed form. From the crossed shape of two piezoelectrics, various conditions of torsional loading can be analyzed by simply measuring load voltage amplitudes and phases. We derive a modeling framework of the cross-shaped piezoelectric sensor under torsional loading to expect the sensing response of the sensors. Also, an experimental setup is established to verify the modeling statement.
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