材料科学
表面粗糙度
触觉传感器
聚偏氟乙烯
压电
压力传感器
表面光洁度
人工智能
分析化学(期刊)
纳米技术
复合材料
计算机科学
机械工程
工程类
化学
有机化学
机器人
聚合物
作者
Xinwang Wang,Yuping Lu,Jiashun Jiang,Chunyu Lv,Hailing Fu,Mengying Xie
出处
期刊:IEEE Sensors Journal
[Institute of Electrical and Electronics Engineers]
日期:2024-03-01
卷期号:24 (5): 7176-7184
被引量:1
标识
DOI:10.1109/jsen.2024.3352284
摘要
Accurate assessment of surface roughness is critical for numerous applications ranging from quality control in manufacturing to material characterization. To achieve a functional capability of roughness perception, a flexible pressure sensor based on Polyvinylidene Fluoride-Ti 3 C 2 (PVDF/MXene) nanocomposite is developed. The sensor consists of electrospun PVDF nanofibers embedded with two-dimensional MXene nanosheets. The MXene enhances the piezoelectric β- phase content of the PVDF up to 97.2% at optimal loading of 2.5 wt%. The PVDF/MXene nanocomposite exhibited high piezoelectric voltage sensitivity up to 0.059 V kPa -1 under applied pressures. Wavelet transform analysis of signals obtained by scanning the sensor on sandpapers of varying roughness showed distinct time-frequency patterns corresponding to different surface roughness levels. Unsupervised dimensionality reduction using t-distributed stochastic neighbor embedding (t-SNE) revealed clustering of roughness data into distinct categories. A convolutional neural network (CNN) classifier achieved 98% accuracy in categorizing the surface roughness based on the sensor signal wavelet transforms. The piezoelectric nanocomposite sensor shows promise for surface metrology applications.
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