Fiber optic tactile sensor for surface roughness recognition by machine learning algorithms
Künye
Keser, S., & Hayber, Ş. E. (2021). Fiber optic tactile sensor for surface roughness recognition by machine learning algorithms. Sensors and Actuators A: Physical, 332, 113071.Özet
In this study, a sensor tip with a metallic hemispherical nozzle tip (MHNT) design based on the Fabry-Perot
interferometer was developed for surface roughness recognition (SRR). Sandpaper samples with ten different arithmetical mean deviations of the surface (Sa) values were used as surfaces to be recognized. The
feature vectors were found by applying the discrete wavelet transform (DWT) to the analog signals obtained
from the sandpaper samples. Machine learning (ML) algorithms K-nearest neighbor (KNN) and support
vector machine (SVM) were used for classification. An in-depth recognition process was carried out using
the classifiers’ different length criteria and kernel types. In the test process, each category consists of two
sub-categories as testing within the training dataset (TWITD) and testing without the training dataset
(TWOTD). The experiments were carried out in a controlled manner with the conveyor belt system (CBS)
and manual. As a result of the experimental studies, the average recognition rates (Rave) for CBS were found
as 84.2% and 81.6% for TWITD and TWOTD, while the Rave for the manual are found as 80% and 77.5% for
TWITD and TWOTD, respectively