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,Yiming Lu,Jiashun Jiang,Chunyu Lv,Hailing Fu,Mengying Xie
标识
DOI:10.1109/jsen.2024.3352284
摘要
The 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-Ti3C2 (PVDF/MXene) nanocomposite is developed. The sensor consists of electrospun PVDF nanofibers embedded with 2-D MXene nanosheets. The MXene enhances the piezoelectric $\beta $ -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. The 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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