Title: An Attachable Electromagnetic Energy Harvester Driven Wireless Sensing System Demonstrating Milling-Processes and Cutter-Wear/Breakage-Condition Monitoring
Authors: Chung, Tien-Kan
Yeh, Po-Chen
Lee, Hao
Lin, Cheng-Mao
Tseng, Chia-Yung
Lo, Wen-Tuan
Wang, Chieh-Min
Wang, Wen-Chin
Tu, Chi-Jen
Tasi, Pei-Yuan
Chang, Jui-Wen
機械工程學系
國際半導體學院
Department of Mechanical Engineering
International College of Semiconductor Technology
Keywords: vibration;electromagnetic;self-powered;energy harvester;wireless;sensing;milling monitoring;attachable;cutter condition
Issue Date: Mar-2016
Abstract: An attachable electromagnetic-energy-harvester driven wireless vibration-sensing system for monitoring milling-processes and cutter-wear/breakage-conditions is demonstrated. The system includes an electromagnetic energy harvester, three single-axis Micro Electro-Mechanical Systems (MEMS) accelerometers, a wireless chip module, and corresponding circuits. The harvester consisting of magnets with a coil uses electromagnetic induction to harness mechanical energy produced by the rotating spindle in milling processes and consequently convert the harnessed energy to electrical output. The electrical output is rectified by the rectification circuit to power the accelerometers and wireless chip module. The harvester, circuits, accelerometer, and wireless chip are integrated as an energy-harvester driven wireless vibration-sensing system. Therefore, this completes a self-powered wireless vibration sensing system. For system testing, a numerical-controlled machining tool with various milling processes is used. According to the test results, the system is fully self-powered and able to successfully sense vibration in the milling processes. Furthermore, by analyzing the vibration signals (i.e., through analyzing the electrical outputs of the accelerometers), criteria are successfully established for the system for real-time accurate simulations of the milling-processes and cutter-conditions (such as cutter-wear conditions and cutter-breaking occurrence). Due to these results, our approach can be applied to most milling and other machining machines in factories to realize more smart machining technologies.
URI: http://dx.doi.org/10.3390/s16030269
http://hdl.handle.net/11536/133823
ISSN: 1424-8220
DOI: 10.3390/s16030269
Journal: SENSORS
Volume: 16
Issue: 3
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