Abstract
Self-powered sensors detect a variety of crucial parameters for industrial processes without any external power source for Industry 4.0. It has capabilities to acquire, analyze, and make decisions on real-time data, these sensors are essential for the advancement of automated production systems and smart factories. In this regard, Triboelectric nanogenerators (TENGs) have potential to provide effective solution as self-powered vibration sensor for continuous monitoring of machine tools. Different techniques have been used to enhance the performance of TENG such as material doping, chemical etching, ion injection, etc. However, laser surface texturing is a low-cost, contact less, efficient technique which can be used for improving the response of TENG-based self-powered vibration sensors. In this context, Laser Induced Backside Texturing is a laser micro texturing approach to improve the electrical performance. It induces waviness and surface roughness, which leads to increased surface charge density and triboelectrification capabilities. In this work, laser micro texturing was performed using with 405 nm wavelength semiconductor diode laser to generate line pattern texture on the Fluorinated Ethylene Propylene (FEP). Laser textures TENG (LT-TENG) was developed with laser textured FEP as tribonegative material and pristine Aluminium as tribopositive material, the open-circuit voltage, short-circuit current and power density achieved 23 %, 41 %, and 47 % enhancement respectively, generating 794 V, 44 µA, and 2371.6 µW/cm2. This increase in output leads to an increased sensitivity of the sensor. When mounted on the machines, LT-TENG was able to detect their working state, vibration frequency, and harmonics i.e. 46, 92, 184 Hz for the vacuum pump and 140, 280, 410 Hz for the heat gun. These detected vibration frequencies, and their harmonics were similar to the frequencies detected by the commercial accelerometer (CTC AC115), confirming potential use of LT-TENG as a low-frequency vibration sensor. The developed LT-TENGs was integrated with the Internet of Things (IoT) supported microcontroller, which collects real-time data for continuous monitoring and analysis for machine health monitoring in smart devices.
Published Version
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