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Directly Printed Low-Cost Nanoparticle Sensor for Vibration Measurement during Milling Process
A real-time, accurate, and reliable process monitoring is a basic and crucial enabler of intelligent manufacturing operation and digital twin applications. In this study, we represent a novel vibration measurement method for workpiece during the milling process using a low-cost nanoparticle vibratio...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7372428/ https://www.ncbi.nlm.nih.gov/pubmed/32610552 http://dx.doi.org/10.3390/ma13132920 |
Sumario: | A real-time, accurate, and reliable process monitoring is a basic and crucial enabler of intelligent manufacturing operation and digital twin applications. In this study, we represent a novel vibration measurement method for workpiece during the milling process using a low-cost nanoparticle vibration sensor. We directly printed the vibration sensor based on silver nanoparticles positioned onto a polyimide substrate using an aerodynamically-focused nanomaterials printing system, which is a direct printing technique for inorganic nanomaterials positioned onto a flexible substrate. Since it does not require any post-process such as chemical etching and heat treatment, a highly sensitive vibration sensor composed of a microscale porous structure was fabricated at a cost of several cents each. Furthermore, accurate and reliable vibration data was obtained by simple and direct attachment to a workpiece. In this study, we discussed the performance of vibration measurement of a fabricated sensor in comparison to a commercial vibration sensor. Using frequency and power spectrum analysis of obtained data, we directly measured the vibration of workpiece during the milling process, according to a process parameter. Lastly, we applied a fabricated sensor for the digital twins of turbine blade manufacturing in which vibration greatly affects the quality of the product to predict the process defects in real-time. |
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