Fiber optic tactile sensor for surface roughness recognition by machine learning algorithms

•Powerful machine learning algorithms recognize signals obtained from Fabry-Perot based Metallic hemispherical nozzle type.•The Fabry-Perot interferometer-based tactile sensor has been used to surface roughness recognition.•The testing process was carried out in two different stages as manual and conveyor belt systems.•The testing process has been extensively carried out.•By choosing sandpaper for surface samples, we have applied other than parallel surfaces. [Display omitted] 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.